From 8dfcc967d6368d88c0bc8489afdea5bdf3594402 Mon Sep 17 00:00:00 2001 From: Ganggao Zhu Date: Fri, 3 Feb 2017 17:21:15 +0100 Subject: [PATCH] reduce unnecessary codes --- Concept_Similarity.ipynb | 113 --- LICENSE | 197 ------ README.md | 4 +- Word_Similarity.ipynb | 2 +- docs/mkdocs.yml | 9 +- docs/sources/category_classify.md | 0 docs/sources/concept_similarity.md | 0 docs/sources/entity_search.md | 0 docs/sources/index.md | 211 +----- docs/sources/installation.md | 0 docs/sources/usage.md | 0 docs/sources/word_similarity.md | 0 sematch/__init__.py | 2 +- sematch/{classification.py => application.py} | 219 +++--- sematch/disambiguation.py | 90 --- sematch/eval/word_similarity/README.md | 29 - sematch/eval/word_similarity/graph_mc.txt | 27 - sematch/eval/word_similarity/graph_rg.txt | 57 -- sematch/eval/word_similarity/graph_simlex.txt | 657 ----------------- .../eval/word_similarity/graph_ws353-sim.txt | 189 ----- sematch/eval/word_similarity/graph_ws353.txt | 321 --------- sematch/eval/word_similarity/noun_mc.txt | 30 - sematch/eval/word_similarity/noun_rg.txt | 65 -- sematch/eval/word_similarity/noun_simlex.txt | 666 ----------------- .../eval/word_similarity/noun_ws353-sim.txt | 201 ------ sematch/eval/word_similarity/noun_ws353.txt | 348 --------- .../word_similarity/results/graph_mc-jcn.txt | 28 - .../word_similarity/results/graph_mc-lch.txt | 28 - .../word_similarity/results/graph_mc-li.txt | 28 - .../word_similarity/results/graph_mc-lin.txt | 28 - .../word_similarity/results/graph_mc-path.txt | 28 - .../word_similarity/results/graph_mc-res.txt | 28 - .../results/graph_mc-res_graph.txt | 28 - .../word_similarity/results/graph_mc-wup.txt | 28 - .../word_similarity/results/graph_rg-jcn.txt | 58 -- .../word_similarity/results/graph_rg-lch.txt | 58 -- .../word_similarity/results/graph_rg-li.txt | 58 -- .../word_similarity/results/graph_rg-lin.txt | 58 -- .../word_similarity/results/graph_rg-path.txt | 58 -- .../word_similarity/results/graph_rg-res.txt | 58 -- .../results/graph_rg-res_graph.txt | 58 -- .../word_similarity/results/graph_rg-wup.txt | 58 -- .../results/graph_simlex-jcn.txt | 658 ----------------- .../results/graph_simlex-lch.txt | 658 ----------------- .../results/graph_simlex-li.txt | 658 ----------------- .../results/graph_simlex-lin.txt | 658 ----------------- .../results/graph_simlex-path.txt | 658 ----------------- .../results/graph_simlex-res.txt | 658 ----------------- .../results/graph_simlex-res_graph.txt | 658 ----------------- .../results/graph_simlex-wup.txt | 658 ----------------- .../results/graph_ws353-jcn.txt | 322 --------- .../results/graph_ws353-lch.txt | 322 --------- .../results/graph_ws353-li.txt | 322 --------- .../results/graph_ws353-lin.txt | 322 --------- .../results/graph_ws353-path.txt | 322 --------- .../results/graph_ws353-res.txt | 322 --------- .../results/graph_ws353-res_graph.txt | 322 --------- .../results/graph_ws353-sim-jcn.txt | 190 ----- .../results/graph_ws353-sim-lch.txt | 190 ----- .../results/graph_ws353-sim-li.txt | 190 ----- .../results/graph_ws353-sim-lin.txt | 190 ----- .../results/graph_ws353-sim-path.txt | 190 ----- .../results/graph_ws353-sim-res.txt | 190 ----- .../results/graph_ws353-sim-res_graph.txt | 190 ----- .../results/graph_ws353-sim-wup.txt | 190 ----- .../results/graph_ws353-wup.txt | 322 --------- .../word_similarity/results/noun_mc-jcn.txt | 31 - .../word_similarity/results/noun_mc-lch.txt | 31 - .../word_similarity/results/noun_mc-li.txt | 31 - .../word_similarity/results/noun_mc-lin.txt | 31 - .../word_similarity/results/noun_mc-path.txt | 31 - .../word_similarity/results/noun_mc-res.txt | 31 - .../results/noun_mc-wpath_mc.txt | 31 - .../results/noun_mc-wpath_rg.txt | 31 - .../results/noun_mc-wpath_simlex.txt | 31 - .../results/noun_mc-wpath_ws353.txt | 31 - .../results/noun_mc-wpath_ws353sim.txt | 31 - .../word_similarity/results/noun_mc-wup.txt | 31 - .../word_similarity/results/noun_rg-jcn.txt | 66 -- 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.../results/noun_simlex-wpath_simlex.txt | 667 ------------------ .../results/noun_simlex-wpath_ws353.txt | 667 ------------------ .../results/noun_simlex-wpath_ws353sim.txt | 667 ------------------ .../results/noun_simlex-wup.txt | 667 ------------------ .../results/noun_ws353-jcn.txt | 349 --------- .../results/noun_ws353-lch.txt | 349 --------- .../word_similarity/results/noun_ws353-li.txt | 349 --------- .../results/noun_ws353-lin.txt | 349 --------- .../results/noun_ws353-path.txt | 349 --------- .../results/noun_ws353-res.txt | 349 --------- .../results/noun_ws353-sim-jcn.txt | 202 ------ .../results/noun_ws353-sim-lch.txt | 202 ------ .../results/noun_ws353-sim-li.txt | 202 ------ .../results/noun_ws353-sim-lin.txt | 202 ------ .../results/noun_ws353-sim-path.txt | 202 ------ .../results/noun_ws353-sim-res.txt | 202 ------ .../results/noun_ws353-sim-wpath_mc.txt | 202 ------ .../results/noun_ws353-sim-wpath_rg.txt | 202 ------ .../results/noun_ws353-sim-wpath_simlex.txt | 202 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----------- .../results/type_ws353-jcn.txt | 212 ------ .../results/type_ws353-jcn_graph.txt | 212 ------ .../results/type_ws353-lch.txt | 212 ------ .../word_similarity/results/type_ws353-li.txt | 212 ------ .../results/type_ws353-lin.txt | 212 ------ .../results/type_ws353-lin_graph.txt | 212 ------ .../results/type_ws353-path.txt | 212 ------ .../results/type_ws353-res.txt | 212 ------ .../results/type_ws353-res_graph.txt | 212 ------ .../results/type_ws353-sim-jcn.txt | 129 ---- .../results/type_ws353-sim-jcn_graph.txt | 129 ---- .../results/type_ws353-sim-lch.txt | 129 ---- .../results/type_ws353-sim-li.txt | 129 ---- .../results/type_ws353-sim-lin.txt | 129 ---- .../results/type_ws353-sim-lin_graph.txt | 129 ---- .../results/type_ws353-sim-path.txt | 129 ---- .../results/type_ws353-sim-res.txt | 129 ---- .../results/type_ws353-sim-res_graph.txt | 129 ---- .../results/type_ws353-sim-wup.txt | 129 ---- .../results/type_ws353-wup.txt | 212 ------ .../results_original/graph_mc.txt | 28 - .../results_original/graph_rg.txt | 58 -- .../results_original/graph_simlex.txt | 658 ----------------- .../results_original/graph_ws353-sim.txt | 190 ----- .../results_original/graph_ws353.txt | 322 --------- .../results_original/noun_mc.txt | 31 - .../results_original/noun_rg.txt | 66 -- .../results_original/noun_simlex.txt | 667 ------------------ .../results_original/noun_ws353-sim.txt | 202 ------ .../results_original/noun_ws353.txt | 349 --------- .../results_original/readme.txt | 3 - .../results_original/type_mc.txt | 19 - .../results_original/type_rg.txt | 42 -- .../results_original/type_simlex.txt | 409 ----------- .../results_original/type_ws353-sim.txt | 129 ---- .../results_original/type_ws353.txt | 212 ------ sematch/eval/word_similarity/type_mc.txt | 18 - sematch/eval/word_similarity/type_rg.txt | 41 -- sematch/eval/word_similarity/type_simlex.txt | 408 ----------- .../eval/word_similarity/type_ws353-sim.txt | 128 ---- sematch/eval/word_similarity/type_ws353.txt | 211 ------ sematch/evaluation.py | 95 +-- sematch/linking.py | 253 ------- sematch/models/dbpedia_type_ic.txt | 6 + sematch/models/yago_type_ic.txt | 1 + sematch/nlp.py | 281 +++----- sematch/ontology.py | 112 --- sematch/search.py | 38 - sematch/semantic/graph.py | 90 ++- sematch/semantic/similarity.py | 290 ++++---- sematch/{ => semantic}/sparql.py | 133 +++- sematch/server.py | 59 -- sematch/static/Blob.js | 177 ----- sematch/static/FileSaver.js | 241 ------- sematch/static/favicon.ico | Bin 1150 -> 0 bytes sematch/static/jquery.steps.min.js | 6 - sematch/static/loading.GIF | Bin 29701 -> 0 bytes sematch/static/logo.png | Bin 136637 -> 0 bytes sematch/static/style.css | 394 ----------- sematch/summarize.py | 0 sematch/templates/base.html | 225 ------ sematch/templates/demo.html | 428 ----------- sematch/templates/sematch.html | 371 ---------- sematch/utility.py | 52 -- tests/test_application.py | 23 + tests/test_classification.py | 21 - tests/test_disambiguation.py | 6 - tests/test_evaluation.py | 33 +- tests/test_graph.py | 16 - tests/test_nlp.py | 22 +- tests/test_search.py | 29 - tests/test_semantic_similarity.py | 53 -- tests/test_similarity.py | 109 +++ tests/test_sparql.py | 12 +- 230 files changed, 773 insertions(+), 44369 deletions(-) delete mode 100644 Concept_Similarity.ipynb delete mode 100644 LICENSE delete mode 100644 docs/sources/category_classify.md delete mode 100644 docs/sources/concept_similarity.md delete mode 100644 docs/sources/entity_search.md delete mode 100644 docs/sources/installation.md delete mode 100644 docs/sources/usage.md delete mode 100644 docs/sources/word_similarity.md rename sematch/{classification.py => application.py} (59%) delete mode 100644 sematch/disambiguation.py delete mode 100644 sematch/eval/word_similarity/README.md delete mode 100644 sematch/eval/word_similarity/graph_mc.txt delete mode 100644 sematch/eval/word_similarity/graph_rg.txt delete mode 100644 sematch/eval/word_similarity/graph_simlex.txt delete 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--- a/Concept_Similarity.ipynb +++ /dev/null @@ -1,113 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Introduction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Concetual similarity based on a specific concept taxonomy is useful for many applications. For example, in document analysis, a conceptual graph can be constructed based on the concepts in the document, so important concepts or the main topics of the document can be identified using graph-based analysis techniques. One type of conceptual graph is concept similarity graph which is constructed from computing semantic similarity between concepts. \n", - "\n", - "To facilitate the computation of semantic similarity, we have implemented a Taxonomy interface and several state of art semantic similarity metrics based on the Taxonomy interfaces. In this notebook, we are going to show how to implement semantic similarity computation using our implementation. Basically, you need to implement the DataTransform interface. We are going to show the implementation of DBpedia ontology classes, Wikipedia category taxonomy, the Medical Subject Headings, using Sematch. Similar method can be applied to other hiearchical concepts such as Open Directory Project, the ACM Term Classification and many others." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### DBpedia Ontology Class Taxonomy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To compute semantic similarity between concepts in DBpedia ontology classes. First, transform the ontology classes into tuples of nodes, edges and labels. Second, use taxonomy module to parse the tuples. Finally, compute the conceptual similarity using ConceptSimilarity module." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0\n", - "0.2\n", - "0.333333333333\n", - "0.241311925619\n", - "0.0\n", - "0.0\n", - "0.2\n" - ] - } - ], - "source": [ - "from sematch.semantic.graph import DataTransform, Taxonomy\n", - "from sematch.semantic.similarity import ConceptSimilarity\n", - "from sematch.ontology import DBpedia\n", - "\n", - "\n", - "class DBpediaDataTransform(DataTransform):\n", - "\n", - " def __init__(self):\n", - " self._ontology = DBpedia()\n", - "\n", - " def transform(self):\n", - " nodes = map(lambda x:x.toPython(), self._ontology.classes)\n", - " node_id = {n:i for i,n in enumerate(nodes)}\n", - " labels = [self._ontology.token(value) for i,value in enumerate(self._ontology.classes)]\n", - " edges = []\n", - " for i, node in enumerate(nodes):\n", - " children = self._ontology.subClass(node)\n", - " children = [child for child in children if child in nodes]\n", - " children_ids = map(lambda x:node_id[x], children)\n", - " for child_id in children_ids:\n", - " edges.append((i, child_id))\n", - " return nodes, labels, edges\n", - "\n", - "concept_sim = ConceptSimilarity(Taxonomy(DBpediaDataTransform()), 'models/dbpedia_type_ic.txt')\n", - "\n", - "c1 = concet_sim.name2concept('species')\n", - "c2 = concet_sim.name2concept('organ')\n", - "print concept_sim.path(c1, c1)\n", - "print concept_sim.path(c1, c2)\n", - "print concept_sim.wup(c1,c2)\n", - "print concept_sim.li(c1, c2)\n", - "print concept_sim.res(c1,c2)\n", - "print concept_sim.jcn(c1, c2)\n", - "print concept_sim.wpath(c1,c2)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 105a222..0000000 --- a/LICENSE +++ /dev/null @@ -1,197 +0,0 @@ - - - -Copyright 2016 Ganggao Zhu UPM-GSI: Group of Intelligent Systems - Universidad Politécnica de Madrid (UPM) - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. - - - - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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However, in accepting such obligations, You may act only - on Your own behalf and on Your sole responsibility, not on behalf - of any other Contributor, and only if You agree to indemnify, - defend, and hold each Contributor harmless for any liability - incurred by, or claims asserted against, such Contributor by reason - of your accepting any such warranty or additional liability. - - END OF TERMS AND CONDITIONS - - \ No newline at end of file diff --git a/README.md b/README.md index 9bc50ee..0e4cb35 100644 --- a/README.md +++ b/README.md @@ -184,7 +184,7 @@ from collections import Counter #query Tom Cruise DBpedia textual description tom = EntityFeatures().features('http://dbpedia.org/resource/Tom_Cruise') #extract common nouns from abstract -words = Extraction().extract_words_sent(tom['abstract']) +words = Extraction().extract_words_doc(tom['abstract']) #lemmatize words to remove duplicates words = list(set(lemmatization(words))) #define a similarity metric @@ -195,7 +195,7 @@ word_graph = SimGraph(words, wns.word_similarity) word_scores = word_graph.page_rank() words, scores =zip(*Counter(word_scores).most_common(10)) print words -(u'action', u'Picture', u'Performance', u'program', u'sport', u'number', u'film', u'series', u'role', u'credit') +(u'action', u'Picture', u'Performance', u'number', u'program', u'sport', u'film', u'role', u'series', u'credit') ``` ------------------ diff --git a/Word_Similarity.ipynb b/Word_Similarity.ipynb index 0d840ed..2933cd6 100644 --- a/Word_Similarity.ipynb +++ b/Word_Similarity.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook records the experiments I have done in the article of \"Computing Semantic Similarity of Concepts in Knowledge Graphs\" published in IEEE Transactions of Data and Knowledge Engineering. If someone is intrested in reproducing the experiments, one can download the Sematch software and use this notebook for reference." + "This notebook records the experiments I have done in the article of \"Computing Semantic Similarity of Concepts in Knowledge Graphs\" published in IEEE Transactions of Knowledge and Data Engineering. If someone is intrested in reproducing the experiments, one can download the Sematch software and use this notebook for reference." ] }, { diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 8040276..37d3d2b 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -9,7 +9,8 @@ site_author: Ganggao Zhu pages: - Home: index.md -- Word similarity: word_similarity.md -- Concept similarity: concept_similarity.md -- Category Classification: category_classify.md -- Entity Search: entity_search.md \ No newline at end of file +- Similarity: similarity.md +- Evaluation: evaluation.md +- NLP: nlp.md +- Application: application.md +- API: api.md diff --git a/docs/sources/category_classify.md b/docs/sources/category_classify.md deleted file mode 100644 index e69de29..0000000 diff --git a/docs/sources/concept_similarity.md b/docs/sources/concept_similarity.md deleted file mode 100644 index e69de29..0000000 diff --git a/docs/sources/entity_search.md b/docs/sources/entity_search.md deleted file mode 100644 index e69de29..0000000 diff --git a/docs/sources/index.md b/docs/sources/index.md index 5b266eb..74d6317 100644 --- a/docs/sources/index.md +++ b/docs/sources/index.md @@ -2,16 +2,14 @@ ------------------ -# Sematch: Semantic Similarity Analysis +## Introduction -## You have just found Sematch - -Sematch is a semantic similarity analysis library, written in Python. Semantic similarity is a very useful metric for many applications that require to quantify the semantic distance between concepts using structural knowledge drawn from conceptual taxonomy and statistical information calculated from textual corpora. Sematch was developed with a focus on enabling semantic similarity analysis in both textual corpora and structural Knowledge Graphs (KGs). +Sematch is a semantic similarity analysis library, written in Python 2.7. Semantic similarity is a very useful metric for many applications that require to quantify the semantic similarity between concepts. Sematch focus on knowledge-based semantic similarity using structural knowledge in taxonomy (e.g. depth, path length, least common subsumer), and statistical information contents (corpus-IC and graph-IC). Sematch provides an integrated framework for the development, evaluation and application of semantic similarity with both textual corpus and structural Knowledge Graphs (KGs). ![logo](img/sematch-motivation.jpg) -This motivation comes from the observation that many textual applications and KG applications share a common pipeline in using semantic similarity analysis. In textual applications, word similarity is measured based on WordNet taxonomy or word cooccurence. Then text similarity (sentence level) is measured by composing word similarities, while document level text could be analysed by identifying important sentences based on text similarity, e.g. TextRank. Obviously, the pipeline starts from measuring concept similarity. Concepts usually represent atomic word meanings and have a hierarchical structure, such as WordNet synsets. Concept similarity are normally quantified using various information such as structural knowledge (e.g. path length, depth, least common subsumer) and statistical knowledge ( information content). +Many applications share a common pipeline in using semantic similarity analysis. In textual applications, word similarity is measured based on WordNet with various semantic similarity metrics. Then text similarity (sentence level) is measured by composing word similarities, while document level text could be analysed by identifying important sentences based on text similarity, e.g. TextRank. This process starts from measuring concept similarity (word meanings or senses which are denoted as synsets in WordNet) which captures word level of meaning. This is normally referred as knowledge-based semantic similarity compared to its conterpart corpus-based approaches that are based on word coocurrence (e.g. Pointwise Mutual Information) or distributional semantics (Explict Semantic Analysis, Word2Vec, GLOVE and etc). Concept similarity are normally quantified using various information such as structural knowledge (e.g. path length, depth, least common subsumer) and statistical knowledge ( information content). KG based application also have similar pipeline. Entities in KGs are similar to words, while entities are described by concepts (e.g. dbo:Movie, dbc:English_opera_singers) which are similar to word meanings (senses). Similar to word similarity, entity similarity can be quantified based on concept similarity. Moreover, those concepts in KGs are usually constructed into hierarchical taxonomies, such as DBpedia ontology class, Wikipedia categories, therefore quantifying concept similarity in WordNet or DBpedia relies on similar semantic information (e.g. path length, depth, least common subsumer, information content) and semantic similarity metrics (e.g. Path, Wu & Palmer,Li, Resnik, Lin, Jiang & Conrad and WPath). Furthermore, as WordNet concepts have been integrated into many modern KGs such as DBpedia, YAGO and BabelNet, it would be convenient to apply conventional semantic similarity analysis to KGs. Consequently, Sematch aims to provide a framework to develop and evaluate semantic similarity metrics for concepts, entities, texts and their applications. @@ -20,212 +18,48 @@ KG based application also have similar pipeline. Entities in KGs are similar to ------------------ +## Installation -## Guiding principles - -The goal of the sematch is to provide several core tools for semantic similarity analysis. Aiming this goal, Sematch has several guiding principles in development. - -- Provide a convenient framework for developing and evaluating semantic similarity metrics and provide easy interface in developing similarity based applications. -- Provide a convenient framework for interacting KGs by providing entity linking and disambiguations, entity feature extraction, since KGs contain various textual descriptions and structural relations. -- Provide a convenient frameowrk for performing semantic similarity analysis such as similarity graph. - - ------------------- -## Getting started: 20 minutes to Sematch - -The core module of Sematch is measuring semantic similarity between concepts that are represeted as concept taxonomies. Word similarity is computed based on the maximum semantic similarity of WordNet concepts. Sematch uses WordNet interface from NLTK so it covers several common similarity metrics such as Path, Wu & Palmer, Resnik, Lin, Jiang & Conrad. Sematch also provides some similarity metrics that are not covered in NLTK, such as Li method and WPath metric. Some common interfaces for semantic information are provided so that novel similarity metrics are easy to implement with Sematch. Moreover, [Open Multilingual WordNet](http://compling.hss.ntu.edu.sg/omw/) is integrated so that both mono-lingual and cross-lingual word similarity are available. - -```python -from sematch.semantic.similarity import WordNetSimilarity -wns = WordNetSimilarity() -dog = wns.word2synset('dog')#e.g. Synset('dog.n.01') -cat = wns.word2synset('cat')#e.g. Synset('cat.n.01') -# Measuring semantic similarity between concepts using Path method -print wns.similarity(dog[0], cat[0], 'path') # 0.2 -# Computing English word similarity using Li method -print wns.word_similarity('dog', 'cat', 'li') # 0.449327301063 -# Computing Spanish word similarity using Lin method -print wns.monol_word_similarity('perro', 'gato', 'spa', 'lin') #0.876800984373 -# Computing Chinese word similarity using Wu & Palmer method -print wns.monol_word_similarity('狗', '猫', 'cmn', 'wup') # 0.857142857143 -# Computing Spanish and English word similarity using Resnik method -print wns.crossl_word_similarity('perro', 'cat', 'spa', 'eng', 'res') #7.91166650904 -# Computing Spanish and Chinese word similarity using Jiang & Conrad method -print wns.crossl_word_similarity('perro', '猫', 'spa', 'cmn', 'jcn') #0.31023804699 -# Computing Chinese and English word similarity using WPath method -print wns.crossl_word_similarity('狗', 'cat', 'cmn', 'eng', 'wpath')#0.593666388463 -``` - -In order to apply similarity metrics to KGs, Sematch contains WordNet and YAGO mappings derived from [YAGO dataset](http://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/downloads/). Consequently, YAGO type URIs can be treated similarly as WordNet synsets. By having graph-based IC, semantic similarity between YAGO types and corresponding synsets used in YAGO can been measured based on the type distribution in DBpedia. - -```python -from sematch.semantic.similarity import YagoTypeSimilarity -yago_sim = YagoTypeSimilarity() -#Mapping a word to yago links in DBpedia -dancer = yago_sim.word2yago('dancer')#'http://dbpedia.org/class/yago/Dancer109989502' -actor = yago_sim.word2yago('actor') #'http://dbpedia.org/class/yago/Actor109765278' -singer = yago_sim.word2yago('singer')#'http://dbpedia.org/class/yago/Singer110599806' -#Mapping a yago link to word net synset -yago_sim.yago2synset(actor[0]) # Synset('actor.n.01') -#Measuring semantic similarity between yago links through WordNet taxonomy -yago_sim.yago_similarity(dancer[0], actor[0], 'wpath')#0.642 -yago_sim.yago_similarity(singer[0], actor[0], 'wpath')#0.544 -#Measuring word similarity based on graph-based IC using yago concepts -#using corpus-based IC from brown corpus -yago_sim.word_similarity('dancer', 'actor', 'wpath')#0.642 -#using graph-based IC from DBpedia -yago_sim.word_similarity('dancer', 'actor', 'wpath_graph')#0.423 -``` -Previous similarity metrics implementations are based on WordNet taxonomy through NLTK's WordNet interface. Apart from English words in WordNet, many real world concepts can be represented hiearchically as concept taonomies, such as DBpedia ontology classes, Wikipedia categories, [Open Diretory Project](https://www.dmoz.org/), [Medical Subject Headings](https://www.nlm.nih.gov/mesh/), [ACM Term ClassificationIn](https://www.acm.org/publications/class-2012) and many others. In order to make it convenient to use, develeop and evaluate semantic similarity metrics in those concept taxonomies, Sematch provides a general purpose taxonomy interface. As those concept taxonomies may be represented in different ways (e.g. RDFS, SKOS), an abstract class is used to transform taxonomies into nodes, edges, and labels. An example of DBpedia ontology class is shown in following code example. - -```python -from sematch.semantic.graph import DataTransform, Taxonomy -from sematch.semantic.similarity import ConceptSimilarity -from sematch.ontology import DBpedia - -class DBpediaDataTransform(DataTransform): - - def __init__(self): - self._ontology = DBpedia() - - def transform(self): - nodes = map(lambda x:x.toPython(), self._ontology.classes) - node_id = {n:i for i,n in enumerate(nodes)} - labels = [self._ontology.token(value) for i,value in enumerate(self._ontology.classes)] - edges = [] - for i, node in enumerate(nodes): - children = self._ontology.subClass(node) - children = [child for child in children if child in nodes] - children_ids = map(lambda x:node_id[x], children) - for child_id in children_ids: - edges.append((i, child_id)) - return nodes, labels, edges - -concept_sim = ConceptSimilarity(Taxonomy(DBpediaDataTransform()), 'models/dbpedia_type_ic.txt') -c1 = concept_sim.name2concept('species') -c2 = concept_sim.name2concept('organ') -print concept_sim.similarity(c1, c2) -``` - -When developing new similarity metrics, proper evaluation is important, whereas sometimes it is tedious. Sematch helps save such efforts by providing a evaluation framework, where similarity metrics are evaluated with common Word Similarity datasets. Futhermore, a statistical test is included by comparing two similarity metrics. - -```python -from sematch.evaluation import WordSimEvaluation -from sematch.semantic.similarity import WordNetSimilarity -wordsim_eval = WordSimEvaluation() -wns = WordNetSimilarity() -#define similarity metrics -lin = lambda x, y: wns.word_similarity(x, y, 'lin') -wpath = lambda x, y: wns.word_similarity_wpath(x, y, 0.8) -#evaluate similarity metrics -wordsim_eval.evaluate_multiple_metrics({'lin':lin, 'wpath':wpath}, 'noun_simlex') -#performa Steiger's Z significance Test -wordsim_eval.statistical_test('wpath', 'lin', 'noun_simlex') -``` - -Although the word similarity correlation measure is the standard way to evaluate the semantic similarity metrics, it relies on human judgements over word pairs which may not have same performance in real applications. Therefore, apart from word similarity evaluation, the Sematch evaluation framework also includes a simple aspect category classification for Aspect Based Sentiment Analysis. - -```python -from sematch.classification import SimCatClassifier -from sematch.evaluation import ABSAEvaluation -from sematch.semantic.similarity import WordNetSimilarity -#defining similarity metric -wns = WordNetSimilarity() -sim_metric_jcn = lambda x, y: wns.word_similarity(x, y, 'jcn') -sim_metric_wpath = lambda x, y: wns.word_similarity_wpath(x, y, 0.9) -#loadding dataset -absa_eval = ABSAEvaluation() -X_train_16, y_train_16 = absa_eval.load_dataset('path to ABSA16_Train') -X_test_16, y_test_16 = absa_eval.load_dataset('path to ABSA16_Test') -#train the classifiers -sim_jcn_classifier = SimCatClassifier.train(zip(X_train_16, y_train_16), sim_metric_jcn) -sim_wpath_classifier = SimCatClassifier.train(zip(X_train_16, y_train_16), sim_metric_wpath) -#evaluate the classifiers -absa_eval.evaluate(X_test_16, y_test_16, sim_jcn_classifier) -absa_eval.evaluate(X_test_16, y_test_16, sim_wpath_classifier) -``` +To install Sematch, you need to install its dependencies and download some datasets it requires. In NLTK, you need to download WordNet 3.0 and Open Mutilingual WordNet. We have included experimental datasets and type mappings with YAGO in models folder inside Sematch. However, some datasets such as Aspect Based Sentitiment Analysis you need to download from their original site. -Like the words can be mapped to WordNet concepts, some muti-word expressions can be mapped to real word entities based on their meanings. Usually, such expressions are composed by a meaningful noun and a specific proper noun, which we can call as type of things. For example, university (common noun) and Madrid (proper noun) refer to all the universities located in Madrid. To conveniently map such experessions to their corresponding entities in DBpedia, you can use sematch `EntitySearch` class. +Currently, you could clone Sematch and setup it in your local. -```python -from sematch.search import EntitySearch -searcher = EntitySearch() -print searcher.search('university in Madrid') -print searcher.search('movies with Tom Cruise') +```shell +git clone http://github.com/gsi-upm/sematch +#after setting up dependencies and downloading datasets, you could test it +cd tests +pytest ``` -The common nouns are linked to YAGO concepts through YAGO mappings, while the proper nouns are linked to DBpedia through our SPARQL module and Linking module. In Linking module, we have implemented several wrappers for Entity Linking tools such as DBpedia Spotlight, TagMe, and BabelNet. The SPARQL module has several classes that can be used to implement useful functionalities for interacting with KGs. - -```python -from sematch.sparql import NameSPARQL -#a simple linker to match proper noun through labels and names. -name_linker = NameSPARQL() -name_linker.name2entities('China')#'http://dbpedia.org/resource/China' -name_linker.name2entities('Spain')#'http://dbpedia.org/resource/Spain' -from sematch.sparql import EntityFeatures -#extract entity information from DBpedia -entity_f = EntityFeatures() -yin_and_yang = entity_f.features('http://dbpedia.org/resource/Yin_and_yang') -``` +------------------ +## Getting started -A simple semantic similarity analysis is applying graph analysis in similarity graph, where nodes are concepts, words, entities and sentences, while edges denote their semantic similarity. Concept similarity graph can be used for graph-based disambiguation. Word similarity graph or Entity similarity graph can be used to rank or extract important words and entities. Sentence similarity graph can be used for extractive document summarization. An example of using similarity graph for extracting important words from a entity description. - -```python -from sematch.semantic.graph import SimGraph -from sematch.semantic.similarity import WordNetSimilarity -from sematch.nlp import Extraction, lemmatization -from sematch.sparql import EntityFeatures -from collections import Counter -#query Tom Cruise DBpedia textual description -tom = EntityFeatures().features('http://dbpedia.org/resource/Tom_Cruise') -#extract common nouns from abstract -words = Extraction().extract_words_sent(tom['abstract']) -#lemmatize words to remove duplicates -words = list(set(lemmatization(words))) -#define a similarity metric -wns = WordNetSimilarity() -#construct a similarity graph -word_graph = SimGraph(words, wns.word_similarity) -#rank words using Page Rank -word_scores = word_graph.page_rank() -words, scores =zip(*Counter(word_scores).most_common(10)) -print words -(u'action', u'Picture', u'Performance', u'program', u'sport', u'number', u'film', u'series', u'role', u'credit') -``` +- Computing Similarity. ------------------- +- Search Entities from DBpedia. +- Classify short text into categories. +- Evaluate Metric. -## Installation +## Software Dependencies Sematch uses the following dependencies: - numpy, scipy, scikit-learn are used for scientific computing and machine learning. -- networkx is used for taxonomy, similarity graph construction and application. +- networkx is used for creating taxonomy, similarity graph construction and application. - nltk is used for natural language processing tasks. - gensim is used for semantic analysis. - RDFLib, SPARQLWrapper, requests are used for interacting with Knowledge Graphs. - Flask is used to implement web services. - pytest is used for unit testing -You should check out the requirements.txt to install the complete dependencies. - -To install Sematch, you need to install its dependencies and download some datasets it requires. In NLTK, you need to download WordNet 3.0 and Open Mutilingual WordNet. We have included experimental datasets and type mappings with YAGO in models folder inside Sematch. However, some datasets such as Aspect Based Sentitiment Analysis you need to download from their original site. - -Currently, you could clone Sematch and setup it in your local. - -```shell -git clone http://github.com/gsi-upm/sematch -#after setting up dependencies and downloading datasets, you could test it -cd tests -pytest -``` +You can check out the full requirements.txt for the complete dependent software and corresponding versions. ------------------ @@ -242,13 +76,12 @@ pytest ## Support You can post bug reports and feature requests in [Github issues](https://github.com/gsi-upm/sematch/issues). -Make sure to read our guidelines first. -This project is still under active development approaching to its goals. The project is mainly maintained by Ganggao Zhu. You can contact him via gzhu [at] dit.upm.es +The project is mainly maintained by Ganggao Zhu. You can contact him via gzhu [at] dit.upm.es ------------------ ## Why this name, Sematch and Logo? -The name of Sematch is composed based on Spanish "se" and English "match". It is also the abbreviation of semantic matching because semantic similarity metrics helps to determine semantic distance of concepts, words, entities, instead of exact matching. +The name of Sematch is composed based on Spanish "se" and English "match". It is also the abbreviation of semantic matching because semantic similarity metrics help to determine semantic distance of concepts, words, entities, instead of exact matching. The logo of Sematch is based on Chinese [Yin and Yang](http://en.wikipedia.org/wiki/Yin_and_yang) which is written in [I Ching](http://en.wikipedia.org/wiki/I_Ching). Somehow, it correlates to 0 and 1 in computer science. diff --git a/docs/sources/installation.md b/docs/sources/installation.md deleted file mode 100644 index e69de29..0000000 diff --git a/docs/sources/usage.md b/docs/sources/usage.md deleted file mode 100644 index e69de29..0000000 diff --git a/docs/sources/word_similarity.md b/docs/sources/word_similarity.md deleted file mode 100644 index e69de29..0000000 diff --git a/sematch/__init__.py b/sematch/__init__.py index c16596d..2f8354e 100644 --- a/sematch/__init__.py +++ b/sematch/__init__.py @@ -1,2 +1,2 @@ -__author__ = 'alex' +__author__ = 'Ganggao Zhu' diff --git a/sematch/classification.py b/sematch/application.py similarity index 59% rename from sematch/classification.py rename to sematch/application.py index f7144ca..90f9679 100644 --- a/sematch/classification.py +++ b/sematch/application.py @@ -1,13 +1,113 @@ - -from sematch.nlp import word_tokenize, lemmatization -from sematch.semantic.similarity import WordVecSimilarity, WordNetSimilarity +#!/usr/bin/python +# -*- coding: utf-8 -*- +# Copyright 2017 Ganggao Zhu- Grupo de Sistemas Inteligentes +# gzhu[at]dit.upm.es +# DIT, UPM +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from sematch.semantic.sparql import NameSPARQL, QueryGraph +from sematch.semantic.similarity import YagoTypeSimilarity from sematch.utility import memoized -from collections import Counter +from sematch.nlp import word_tokenize, word_process, Extraction import numpy as np +import itertools +from collections import Counter -class SimCatClassifier: +class Matcher: + + """This class is used for concept based entity match in DBpedia""" + + def __init__(self, result_limit=5000, expansion=True, show_query=False): + self._expansion = expansion + self._show_query = show_query + self._linker = NameSPARQL() + self._extracter = Extraction() + self._yago = YagoTypeSimilarity() + self._query_graph = QueryGraph(result_limit) + + def type_links(self, word, lang='eng'): + synsets = self._yago.multilingual2synset(word, lang=lang) + if self._expansion: + synsets = list(set(itertools.chain.from_iterable([self._yago.synset_expand(s) for s in synsets]))) + links = [] + for s in synsets: + link_dic = {} + link_dic['name'] = s.name() + link_dic['gloss'] = s._definition + link_dic['lemma'] = ' '.join(s._lemma_names) + concept_link = [] + yago_link = self._yago.synset2yago(s) + dbpedia_link = self._yago.synset2dbpedia(s) + concept_link.append(yago_link) if yago_link else None + concept_link.append(dbpedia_link) if dbpedia_link else None + link_dic['lod'] = concept_link + if link_dic['lod']: + links.append(link_dic) + return links + + def query_process(self, query): + """ + Process query into concept (common noun) and entity (proper noun). Link them + to Knowledge Graph uri links respectively. + :param query: short text query + :return: tuple of concepts and entities in uris. + """ + entities = self._extracter.extract_chunks_sent(query) + entity_filter = list(itertools.chain.from_iterable([e.lower().split() for e in entities])) + entity_filter = set(entity_filter) + concepts = list(set(self._extracter.extract_nouns(query))) + concepts = [c for c in concepts if c not in entity_filter] + concept_uris = [list(itertools.chain.from_iterable([s['lod'] for s in self.type_links(c)])) for c in concepts] + concept_uris = list(itertools.chain.from_iterable(concept_uris)) + entity_uris = list(itertools.chain.from_iterable(map(self._linker.name2entities, entities))) + return list(set(concept_uris)), list(set(entity_uris)) + + def match_type(self, query, lang='eng'): + lang_map = {'eng':'en','spa':'es', 'cmn':'zh'} + result_lang = lang_map[lang] + words = query.split() + concept_uris = [] + for w in words: + concepts = list(itertools.chain.from_iterable([s['lod'] for s in self.type_links(w,lang)])) + concept_uris.extend(concepts) + concept_uris = list(set(concept_uris)) + results = [] + for i in xrange(0, len(concept_uris), 5): + results.extend(self._query_graph.type_query(concept_uris[i:i + 5], result_lang, self._show_query)) + result_dic = {} + for res in results: + if res['uri'] not in result_dic: + result_dic[res['uri']] = res + return [result_dic[key] for key in result_dic.keys()] + + def match_entity_type(self, query): + results = [] + concepts, entities = self.query_process(query) + for e in entities: + for i in xrange(0, len(concepts), 5): + results.extend(self._query_graph.type_entity_query(concepts[i:i + 5], e, self._show_query)) + result_dic = {} + for res in results: + if res['uri'] not in result_dic: + result_dic[res['uri']] = res + result = [result_dic[key] for key in result_dic.keys()] + return result + + +class SimClassifier: """ This class implements similarity based category classifiers. """ @@ -39,9 +139,10 @@ def train(cls, corpus, sim_metric, feature_num=5, sim_model='weighted'): Compute the weight for each feature token in each category The weight is computed as token_count / total_feature_count ''' + print "Training..." cat_word = {} for sent, cat in corpus: - cat_word.setdefault(cat, []).extend(lemmatization(word_tokenize(sent))) + cat_word.setdefault(cat, []).extend(word_process(word_tokenize(sent))) features = {cat: Counter(cat_word[cat]) for cat in cat_word} labels = features.keys() cat_features = {} @@ -113,7 +214,7 @@ def classify_single(self, sent, feature_model='max'): :param model: similarity combination model 'max', 'sum'. Default is 'max' :return: the correct category label. """ - feature_words = list(set(lemmatization(word_tokenize(sent)))) + feature_words = list(set(word_process(word_tokenize(sent)))) score = {} for c in self._categories: if feature_model == 'max': @@ -126,6 +227,7 @@ def classify(self, X, feature_model='max'): return [self.classify_single(x, feature_model) for x in X] + from sklearn.svm import LinearSVC from sklearn.base import BaseEstimator, TransformerMixin from sklearn.pipeline import Pipeline @@ -149,20 +251,15 @@ class TextPreprocessor(BaseEstimator, TransformerMixin): """ Transform input text into feature representation """ - def __init__(self, corpus, feature_num=10, model='onehot', - wn_method='path', - vec_file='models/GoogleNews-vectors-negative300.bin', - binary=True): + def __init__(self, corpus, word_sim_metric, feature_num=10, model='sim'): """ :param corpus: use a corpus to train a vector representation :param feature_num: number of dimensions - :param model: onehot or wordnet or word2vec or both + :param model: onehot or sim """ self._model = model - self._wn_method = wn_method + self._word_sim = word_sim_metric self._features = self.extract_features(corpus, feature_num) - self._wns = WordNetSimilarity() if model == 'wordnet' or model == 'both' else None - self._wvs = WordVecSimilarity(vec_file, binary) if model == 'word2vec' or model == 'both' else None def fit(self, X, y=None): return self @@ -173,7 +270,7 @@ def inverse_transform(self, X): def extract_features(self, corpus, feature_num=10): cat_word = {} for sent, cat in corpus: - cat_word.setdefault(cat, []).extend(lemmatization(word_tokenize(sent))) + cat_word.setdefault(cat, []).extend(word_process(word_tokenize(sent))) features = {cat: Counter(cat_word[cat]) for cat in cat_word} feature_words = [] for c, f in features.iteritems(): @@ -182,11 +279,8 @@ def extract_features(self, corpus, feature_num=10): feature_words = set(feature_words) return feature_words - def similarity(self, tokens, feature, method='wordnet'): - if method == 'wordnet': - sim = lambda x: self._wns.word_similarity(feature, x, self._wn_method) - else: - sim = lambda x: self._wvs.word_similarity(feature, x) + def similarity(self, tokens, feature): + sim = lambda x: self._word_sim(feature, x) return max(map(sim, tokens) + [0.0]) def unigram_features(self, tokens): @@ -196,55 +290,31 @@ def unigram_features(self, tokens): features['contains({})'.format(f)] = (f in words) return features - def wordnet_features(self, tokens): + def sim_features(self, tokens): words = set(tokens) features = {} for f in self._features: - features['wns({})'.format(f)] = self.similarity(words, f) - return features - - def word2vec_features(self, tokens): - words = set(tokens) - features = {} - for f in self._features: - features['w2v({})'.format(f)] = self.similarity(words, f, method='word2vec') - return features - - def semantic_features(self, tokens): - words = set(tokens) - features = {} - for f in self._features: - features['wns({})'.format(f)] = self.similarity(words, f) - features['w2v({})'.format(f)] = self.similarity(words, f, method='word2vec') + features['sim({})'.format(f)] = self.similarity(words, f) return features def transform(self, X): - tokenize = lambda x: lemmatization(word_tokenize(x)) + tokenize = lambda x: word_process(word_tokenize(x)) X_tokens = map(tokenize, X) if self._model == 'onehot': return map(self.unigram_features, X_tokens) - elif self._model == 'wordnet': - return map(self.wordnet_features, X_tokens) - elif self._model == 'word2vec': - return map(self.word2vec_features, X_tokens) - elif self._model == 'both': - return map(self.semantic_features, X_tokens) + else: + return map(self.sim_features, X_tokens) -class SimCatSVMClassifier: +class SimSVMClassifier: def __init__(self, labels, model): self._labels = labels self._model = model @classmethod - def train(cls, X, y, classifier=LinearSVC, - feature_num=10, - feature='onehot', - wn_method='path', - vec_file='models/GoogleNews-vectors-negative300.bin', - binary=True, - verbose=True): + def train(cls, X, y, word_sim_metric, classifier=LinearSVC, + feature_num=10, feature_type='sim', verbose=True): if isinstance(classifier, type): classifier = classifier() @@ -257,7 +327,7 @@ def build(): corpus = zip(X, y) model = Pipeline([ - ('preprocessor', TextPreprocessor(corpus, feature_num, feature, wn_method, vec_file, binary)), + ('preprocessor', TextPreprocessor(corpus, word_sim_metric, feature_num, feature_type)), ('vectorizer', DictVectorizer()), ('classifier', classifier), ]) @@ -274,44 +344,3 @@ def build(): def classify(self, X): predicted = self._model.predict(X) return list(self._labels.inverse_transform(predicted)) - - - -class SimpleSVMClassifier: - - def __init__(self, labels, vectorizer, classifier): - self._labels = labels - self._vectorizer = vectorizer - self._classifier = classifier - - @classmethod - def train(cls, X, y, classifier=LinearSVC, model='bow'): - """ - :param X: - :param y: - :param classifier: - :param model: bow or tfidf - :return: - """ - tokenize = lambda x: lemmatization(word_tokenize(x)) - labels = LabelEncoder() - y_train = labels.fit_transform(y) - vectorizer = CountVectorizer(tokenizer=tokenize) \ - if model == 'bow' else TfidfVectorizer(tokenizer=tokenize) - X_train = vectorizer.fit_transform(X) - if isinstance(classifier, type): - classifier = classifier() - classifier.fit_transform(X_train, y_train) - return cls(labels, vectorizer, classifier) - - def classify(self, X): - X_test = self._vectorizer.transform(X) - predicted = self._classifier.predict(X_test) - return list(self._labels.inverse_transform(predicted)) - - - - - - - diff --git a/sematch/disambiguation.py b/sematch/disambiguation.py deleted file mode 100644 index 7aa77db..0000000 --- a/sematch/disambiguation.py +++ /dev/null @@ -1,90 +0,0 @@ -from sematch.semantic.similarity import WordNetSimilarity -from sematch.semantic.graph import SimGraph -from sematch.nlp import word_tokenize, lemmatization - -from collections import Counter -import itertools -import random - - -class WSD: - - def __init__(self, wsd_method='maxsim', sim_name='wpath'): - ''' - wsd_methods = ['random_sense','first','frequent','maxsim', 'graph', 'lesk', 'naive'] - sim_name = ['path', 'lch', 'wup', 'li', 'res', 'lin', 'jcn', 'wpath'] - ''' - self._method = wsd_method - self._sim_name = sim_name - self._wn_sim = WordNetSimilarity() - - def disambiguate_graph(self, sentence): - words_origin = word_tokenize(sentence) - #extract words that have a synset in WordNet, currently support NOUN. - words = [w for w in words_origin if self._wn_sim.word2synset(w)] - # map words to synsets - words_synsets = {w:self._wn_sim.word2synset(w) for w in words} - # construct sets list - synsets = list(itertools.chain.from_iterable([words_synsets[w] for w in words])) - # remove duplicate synsets - synsets = list(set(synsets)) - # define semantic similarity metric - sim_metric = lambda x, y: self._wn_sim.similarity(x, y, self._sim_name) - # construct similarity graphs - sim_graph = SimGraph(synsets, sim_metric) - # get pagerank scores of synsets - rank_scores = sim_graph.page_rank() - results = [] - for w in words_origin: - if w in words: - candidate_scores = {s:rank_scores[s] for s in words_synsets[w]} - results.append((w, Counter(candidate_scores).most_common(1)[0][0])) - else: - results.append((w, None)) - return results - - def classify(self, featureset): - context = featureset['context'] - senses = featureset['senses'] - return self.max_senses(context, senses) - - def context2words(self, sent): - words = word_tokenize(sent.lower()) - words = [w for w in words if len(w) > 2] - return lemmatization(words) - - def random_sense(self, word): - senses = self._wn_sim.word2synset(word) - return random.choice(senses) - - def first_sense(self, word): - senses = self._wn_sim.word2synset(word) - return senses[0] - - def word_sense_similarity(self, word, sense): - word_senses = self._wn_sim.word2synset(word) - scorer = lambda x:self._wn_sim.similarity(x, sense, self._sim_name) - sim_scores = map(scorer, word_senses) + [0.0] - return max(sim_scores) - - def max_senses(self, context, senses): - if len(senses) == 1: - return senses[0] - context_words = self.context2words(context) - result = {} - for ss in senses: - scorer = lambda x: self.word_sense_similarity(x, ss) - sim_score = sum(map(scorer, context_words)) - result[ss] = sim_score - return Counter(result).most_common(1)[0][0] - - def max_sim(self, context, word): - senses = self._wn_sim.word2synset(word) - return self.max_senses(context, senses) - - def lesk(self, context, word): - from nltk.wsd import lesk as nltk_lesk - context_words = self.context2words(context) - return nltk_lesk(context_words, word, 'n') - - diff --git a/sematch/eval/word_similarity/README.md b/sematch/eval/word_similarity/README.md deleted file mode 100644 index 1058026..0000000 --- a/sematch/eval/word_similarity/README.md +++ /dev/null @@ -1,29 +0,0 @@ - -###Introduction - -We have collected some word similarity datasets for measuring semantic similarity metrics. - -I process them and provide a uniformed framework to evaluate different semantic measures. - -###[wordsim353](http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/) - -Lev Finkelstein, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin, "Placing Search in Context: The Concept Revisited", ACM Transactions on Information Systems, 20(1):116-131, January 2002 - -###[wordsim353 similarity and relatedness](http://alfonseca.org/eng/research/wordsim353.html) - -Eneko Agirre, Enrique Alfonseca, Keith Hall, Jana Kravalova, Marius Pasca, Aitor Soroa, A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches, In Proceedings of NAACL-HLT 2009. - -###[Rubenstein and Goodenough](http://www.cs.cmu.edu/~mfaruqui/word-sim/EN-RG-65.txt) - -Herbert Rubenstein and John B. Goodenough. 1965. Contextual correlates of synonymy. Commun. ACM 8, 10 (October 1965), 627-633. DOI=10.1145/365628.365657 - -###[Miller and Charles](http://www.cs.cmu.edu/~mfaruqui/word-sim/EN-MC-30.txt) - -Miller, George A., and Walter G. Charles. "Contextual correlates of semantic similarity." Language and cognitive processes 6.1 (1991): 1-28. - -###[SimLex-999](http://www.cl.cam.ac.uk/~fh295/simlex.html) - -SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation. 2014. Felix Hill, Roi Reichart and Anna Korhonen. Preprint pubslished on arXiv. arXiv:1408.3456 - - -NOTE: the folder results contains the experiment results we performed in the word similarity evaluation notebook, while the result original contains the results we got when composing the article. The difference comes from the software version we use and the update of DBpedia data. \ No newline at end of file diff --git a/sematch/eval/word_similarity/graph_mc.txt b/sematch/eval/word_similarity/graph_mc.txt deleted file mode 100644 index e548c1e..0000000 --- a/sematch/eval/word_similarity/graph_mc.txt +++ /dev/null @@ -1,27 +0,0 @@ -car automobile 3.92 -gem jewel 3.84 -journey voyage 3.84 -boy lad 3.76 -coast shore 3.7 -asylum madhouse 3.61 -magician wizard 3.5 -furnace stove 3.11 -food fruit 3.08 -bird cock 3.05 -bird crane 2.97 -tool implement 2.95 -brother monk 2.82 -lad brother 1.66 -crane implement 1.68 -monk oracle 1.1 -cemetery woodland 0.95 -food rooster 0.89 -coast hill 0.87 -forest graveyard 0.84 -shore woodland 0.63 -monk slave 0.55 -coast forest 0.42 -lad wizard 0.42 -chord smile 0.13 -glass magician 0.11 -noon string 0.008 diff --git a/sematch/eval/word_similarity/graph_rg.txt b/sematch/eval/word_similarity/graph_rg.txt deleted file mode 100644 index 7821cc5..0000000 --- a/sematch/eval/word_similarity/graph_rg.txt +++ /dev/null @@ -1,57 +0,0 @@ -gem jewel 3.94 -automobile car 3.92 -cemetery graveyard 3.88 -cushion pillow 3.84 -boy lad 3.82 -cock rooster 3.68 -implement tool 3.66 -forest woodland 3.65 -coast shore 3.6 -autograph signature 3.59 -journey voyage 3.58 -serf slave 3.46 -glass tumbler 3.45 -cord string 3.41 -hill mound 3.29 -magician wizard 3.21 -furnace stove 3.11 -asylum madhouse 3.04 -brother monk 2.74 -food fruit 2.69 -bird cock 2.63 -bird crane 2.63 -oracle sage 2.61 -sage wizard 2.46 -brother lad 2.41 -crane implement 2.37 -magician oracle 1.82 -glass jewel 1.78 -cemetery mound 1.69 -hill woodland 1.48 -crane rooster 1.41 -furnace implement 1.37 -coast hill 1.26 -bird woodland 1.24 -cemetery woodland 1.18 -food rooster 1.09 -forest graveyard 1.0 -lad wizard 0.99 -mound shore 0.97 -automobile cushion 0.97 -boy sage 0.96 -monk oracle 0.91 -shore woodland 0.9 -coast forest 0.85 -asylum cemetery 0.79 -monk slave 0.57 -cushion jewel 0.45 -boy rooster 0.44 -glass magician 0.44 -graveyard madhouse 0.44 -asylum monk 0.39 -asylum fruit 0.19 -mound stove 0.14 -automobile wizard 0.11 -fruit furnace 0.05 -noon string 0.04 -cord smile 0.02 diff --git a/sematch/eval/word_similarity/graph_simlex.txt b/sematch/eval/word_similarity/graph_simlex.txt deleted file mode 100644 index 39fd971..0000000 --- a/sematch/eval/word_similarity/graph_simlex.txt +++ /dev/null @@ -1,657 +0,0 @@ -wife husband 2.3 -book text 6.35 -groom bride 3.17 -night day 1.88 -south north 2.2 -plane airport 3.65 -uncle aunt 5.5 -horse mare 8.33 -bottom top 0.7 -friend buddy 8.78 -student pupil 9.35 -world globe 6.67 -leg arm 2.88 -plane jet 8.1 -woman man 3.33 -horse colt 7.07 -actress actor 7.12 -teacher instructor 9.25 -movie film 8.87 -bird hawk 7.85 -money salary 7.88 -dog cat 1.75 -area region 9.47 -navy army 6.43 -book literature 7.53 -clothes closet 3.27 -sunset sunrise 2.47 -child adult 2.98 -cow cattle 9.52 -book story 5.63 -winter summer 2.38 -taxi cab 9.2 -tree maple 5.53 -bed bedroom 3.4 -roof ceiling 7.58 -disease infection 7.15 -arm shoulder 4.85 -sheep lamb 8.42 -lady gentleman 3.42 -boat anchor 2.25 -priest monk 6.28 -toe finger 4.68 -river stream 7.3 -anger fury 8.73 -date calendar 4.42 -sea ocean 8.27 -second minute 4.62 -hand thumb 3.88 -wood log 7.3 -mud dirt 7.32 -hallway corridor 9.28 -way manner 7.62 -mouse cat 1.12 -cop sheriff 9.05 -death burial 4.93 -music melody 6.98 -beer alcohol 7.5 -mouth lip 7.1 -storm hurricane 6.38 -tax income 2.38 -flower violet 6.95 -paper cardboard 5.38 -floor ceiling 1.73 -beach seashore 8.33 -rod curtain 3.03 -hound fox 2.38 -street alley 5.48 -boat deck 4.28 -car horn 2.57 -friend guest 4.25 -employer employee 3.65 -hand wrist 3.97 -ball cannon 2.58 -alcohol brandy 6.98 -victory triumph 8.98 -telephone booth 3.63 -door doorway 5.4 -motel inn 8.17 -clothes cloth 5.47 -steak meat 7.47 -nail thumb 3.55 -band orchestra 7.08 -book bible 5.0 -business industry 7.02 -winter season 6.27 -decade century 3.48 -alcohol gin 8.65 -hat coat 2.67 -window door 3.33 -arm wrist 3.57 -house apartment 5.8 -glass crystal 6.27 -wine brandy 5.15 -creator maker 9.62 -dinner breakfast 3.33 -arm muscle 3.72 -bubble suds 8.57 -bread flour 3.33 -death tragedy 5.8 -absence presence 0.4 -gun cannon 5.68 -grass blade 4.57 -ball basket 1.67 -hose garden 1.67 -boy kid 7.5 -church choir 2.95 -clothes drawer 3.02 -tower bell 1.9 -father parent 7.07 -school grade 4.42 -parent adult 5.37 -bar jail 1.9 -car highway 3.4 -door cellar 1.97 -army legion 5.95 -metal aluminum 7.25 -chair bench 6.67 -cloud fog 6.0 -boy son 6.75 -water ice 6.47 -bed blanket 3.02 -attorney lawyer 9.35 -area zone 8.33 -business company 9.02 -clothes fabric 5.87 -sweater jacket 7.15 -money capital 6.67 -hand foot 4.17 -alcohol cocktail 6.73 -yard inch 3.78 -molecule atom 6.45 -lens camera 4.28 -meal dinner 7.15 -eye tear 3.55 -god devil 1.8 -loop belt 3.1 -rat mouse 7.78 -motor engine 8.65 -car cab 7.42 -cat lion 6.75 -size magnitude 6.33 -reality fantasy 1.03 -door gate 5.25 -cat pet 5.95 -tin aluminum 6.42 -bone jaw 4.17 -cereal wheat 3.75 -house key 1.9 -blood flesh 4.28 -door corridor 3.73 -god spirit 7.3 -capability competence 7.62 -abundance plenty 8.97 -sofa chair 6.67 -wall brick 4.68 -horn drum 2.68 -organ liver 6.15 -strength might 7.07 -phrase word 5.48 -band parade 3.92 -stomach waist 5.9 -cloud storm 5.6 -joy pride 5.0 -noise rattle 6.17 -rain mist 5.97 -beer beverage 5.42 -man uncle 3.92 -apple juice 2.88 -intelligence logic 6.5 -communication language 7.47 -mink fur 6.83 -mob crowd 7.85 -shore coast 8.83 -wire cord 7.62 -bird turkey 6.58 -bed crib 7.3 -competence ability 7.5 -cloud haze 7.32 -supper meal 7.53 -bar cage 2.8 -water salt 1.3 -sense intuition 7.68 -situation condition 6.58 -crime theft 7.53 -style fashion 8.5 -boundary border 9.08 -arm body 4.05 -boat car 2.37 -sandwich lunch 6.3 -bride princess 2.8 -heroine hero 8.78 -car gauge 1.13 -insect bee 6.07 -crib cradle 8.55 -animal person 3.05 -marijuana herb 6.5 -bed hospital 0.92 -cheek tongue 4.52 -disc computer 3.2 -curve angle 3.33 -grass moss 5.0 -school law 1.13 -foot head 2.3 -mother guardian 6.5 -orthodontist dentist 8.27 -alcohol whiskey 7.27 -mouth tooth 6.3 -breakfast bacon 4.37 -bathroom bedroom 3.4 -plate bowl 5.23 -meat bacon 5.8 -air helium 3.63 -worker employer 5.37 -body chest 4.45 -son father 3.82 -heart surgery 1.08 -woman secretary 1.98 -man father 4.83 -beach island 5.6 -story topic 5.0 -game fun 3.42 -weekend week 4.0 -couple pair 8.33 -woman wife 5.72 -sheep cattle 4.77 -purse bag 8.33 -ceiling cathedral 2.42 -bean coffee 5.15 -wood paper 2.88 -top side 1.9 -crime fraud 5.65 -pain harm 5.38 -lover companion 5.97 -evening dusk 7.78 -father daughter 2.62 -wine liquor 7.85 -cow goat 2.93 -belief opinion 7.7 -reality illusion 1.42 -pact agreement 9.02 -wealth poverty 1.27 -accident emergency 4.93 -battle conquest 7.22 -friend teacher 2.62 -illness infection 6.9 -game trick 2.32 -brother son 3.48 -aunt nephew 3.1 -worker mechanic 4.92 -doctor orthodontist 5.58 -oak maple 6.03 -bee queen 3.27 -car bicycle 3.47 -goal quest 5.83 -august month 5.53 -army squad 5.08 -cloud weather 4.87 -physician doctor 8.88 -canyon valley 6.75 -river valley 1.67 -sun sky 2.27 -target arrow 3.25 -chocolate pie 2.27 -circumstance situation 7.85 -opinion choice 5.43 -rhythm melody 6.12 -gut nerve 4.93 -day dawn 5.47 -cattle beef 7.03 -doctor professor 4.65 -arm vein 3.65 -room bath 3.33 -corporation business 9.02 -fun football 1.97 -hill cliff 4.28 -bone ankle 3.82 -apple candy 2.08 -helper maid 5.58 -leader manager 7.27 -lemon tea 1.6 -bee ant 2.78 -basketball baseball 4.92 -rice bean 2.72 -bed furniture 6.08 -emotion passion 7.72 -anarchy chaos 7.93 -crime violation 7.12 -machine engine 5.58 -beach sea 4.68 -alley bowl 1.53 -jar bottle 7.83 -strength capability 5.28 -seed mustard 3.48 -guitar drum 3.78 -opinion idea 5.7 -north west 3.63 -diet salad 2.98 -mother wife 3.02 -dad mother 3.55 -captain sailor 5.0 -meter yard 5.6 -beer champagne 4.45 -motor boat 2.57 -card bridge 1.97 -science psychology 4.92 -sinner saint 1.6 -destruction construction 0.98 -crowd bunch 7.42 -beach reef 3.77 -man child 4.13 -bread cheese 1.95 -champion winner 8.73 -celebration ceremony 7.72 -menu order 3.62 -king princess 3.27 -wealth prestige 6.07 -endurance strength 6.58 -danger threat 8.78 -god priest 4.5 -men fraternity 3.13 -buddy companion 8.65 -teacher helper 4.28 -body stomach 3.93 -tongue throat 3.1 -house carpet 1.38 -intelligence skill 5.35 -journey conquest 4.72 -god prey 1.23 -brother soul 0.97 -adversary opponent 9.05 -death catastrophe 4.13 -monster demon 6.95 -day morning 4.87 -man victor 1.9 -friend guy 3.88 -song story 3.97 -ray sunshine 6.83 -guy stud 5.83 -chicken rice 1.43 -box elevator 1.32 -butter potato 1.22 -apartment furniture 1.28 -lake swamp 4.92 -salad vinegar 1.13 -flower bulb 4.48 -cloud mist 6.67 -driver pilot 6.28 -sugar honey 5.13 -body shoulder 2.88 -idea image 3.55 -father brother 4.2 -moon planet 5.87 -ball costume 2.32 -rail fence 5.22 -room bed 2.35 -flower bush 4.25 -bone knee 4.17 -arm knee 2.75 -bottom side 2.63 -vessel vein 5.15 -cat rabbit 2.37 -meat sandwich 2.35 -belief concept 5.08 -intelligence insight 5.9 -attention interest 7.22 -attitude confidence 4.35 -right justice 7.05 -argument agreement 1.45 -depth magnitude 6.12 -medium news 3.65 -winner candidate 2.78 -birthday date 5.08 -fee payment 7.15 -bible hymn 5.15 -exit doorway 5.5 -man sentry 3.25 -aisle hall 6.35 -whiskey gin 6.28 -blood marrow 3.4 -oil mink 1.23 -floor deck 5.55 -roof floor 2.62 -door floor 1.67 -shoulder head 3.42 -wagon carriage 7.7 -car carriage 5.13 -elbow ankle 3.13 -wealth fame 4.02 -sorrow shame 4.77 -administration management 7.25 -communication conversation 8.02 -pollution atmosphere 4.25 -anatomy biology 5.33 -college profession 3.12 -book topic 2.07 -formula equation 7.95 -book information 5.0 -boy partner 1.9 -sky universe 4.68 -population people 7.68 -college class 4.13 -chief mayor 4.85 -rabbi minister 7.62 -meter inch 5.08 -polyester cotton 5.63 -lawyer banker 1.88 -violin instrument 6.58 -camp cabin 4.2 -pot appliance 2.53 -linen fabric 7.47 -whiskey champagne 5.33 -girl child 5.38 -cottage cabin 7.72 -bird hen 7.03 -racket noise 8.1 -sunset evening 5.98 -drizzle rain 9.17 -adult baby 2.22 -charcoal coal 7.63 -body spine 4.78 -head nail 2.47 -log timber 8.05 -spoon cup 2.02 -body nerve 3.13 -man husband 5.32 -bone neck 2.53 -frustration anger 6.5 -river sea 5.72 -task job 8.87 -club society 5.23 -reflection image 7.27 -prince king 5.92 -snow weather 5.48 -people party 2.2 -boy brother 6.67 -root grass 3.55 -brow eye 3.82 -money pearl 2.1 -money diamond 3.42 -vehicle bus 6.47 -cab bus 5.6 -house barn 4.33 -finger palm 3.33 -car bridge 0.95 -effort difficulty 4.45 -fact insight 4.77 -job management 3.97 -cancer sickness 7.93 -word newspaper 2.47 -composer writer 6.58 -actor singer 4.52 -shelter hut 6.47 -bathroom kitchen 3.1 -cabin hut 6.53 -door kitchen 1.67 -value belief 7.07 -wisdom intelligence 7.47 -ignorance intelligence 1.5 -happiness luck 2.38 -idea scheme 6.75 -mood emotion 8.12 -happiness peace 6.03 -despair misery 7.22 -logic arithmetic 3.97 -denial confession 1.03 -argument criticism 5.08 -aggression hostility 8.48 -hysteria confusion 6.33 -chemistry theory 3.17 -trial verdict 3.33 -comfort safety 5.8 -confidence self 3.12 -vision perception 6.88 -era decade 5.4 -biography fiction 1.38 -discussion argument 5.48 -code symbol 6.03 -danger disease 3.0 -accident catastrophe 5.9 -journey trip 8.88 -activity movement 7.15 -gossip news 5.22 -father god 3.57 -action course 5.45 -fever illness 7.65 -aviation flight 8.18 -game action 4.85 -molecule air 3.05 -home state 2.58 -word literature 4.77 -adult guardian 6.9 -newspaper information 5.65 -cousin uncle 4.63 -author reader 1.6 -guy partner 3.57 -area corner 2.07 -ballad song 7.53 -wall decoration 2.62 -word page 2.92 -nurse scientist 2.08 -politician president 7.38 -president mayor 5.68 -book essay 4.72 -man warrior 4.72 -article journal 6.18 -breakfast supper 4.4 -crowd parade 3.93 -aisle hallway 6.75 -teacher rabbi 4.37 -hip lip 1.43 -book article 5.43 -room cell 4.58 -box booth 3.8 -daughter kid 4.17 -limb leg 6.9 -liver lung 2.7 -classroom hallway 2.0 -mountain ledge 3.73 -car elevator 1.03 -bed couch 3.42 -clothes button 2.3 -clothes coat 5.35 -kidney organ 6.17 -apple sauce 1.43 -chicken steak 3.73 -car hose 0.87 -tobacco cigarette 7.5 -student professor 1.95 -baby daughter 5.0 -pipe cigar 6.03 -milk juice 4.05 -box cigar 1.25 -apartment hotel 3.33 -cup cone 3.17 -horse ox 3.02 -throat nose 2.8 -bone teeth 4.17 -bone elbow 3.78 -bacon bean 1.22 -cup jar 5.13 -proof fact 7.3 -appointment engagement 6.75 -birthday year 1.67 -word clue 2.53 -author creator 8.02 -atom carbon 3.1 -archbishop bishop 7.05 -letter paragraph 4.0 -page paragraph 3.03 -steeple chapel 7.08 -muscle bone 3.65 -muscle tongue 5.0 -boy soldier 2.15 -belly abdomen 8.13 -guy girl 3.33 -bed chair 3.5 -clothes jacket 5.15 -gun knife 3.65 -tin metal 5.63 -bottle container 7.93 -hen turkey 6.13 -meat bread 1.67 -arm bone 3.83 -neck spine 5.32 -apple lemon 4.05 -agony grief 7.63 -assignment task 8.7 -night dawn 2.95 -dinner soup 3.72 -calf bull 4.93 -snow storm 4.8 -nail hand 3.42 -dog horse 2.38 -arm neck 1.58 -ball glove 1.75 -flu fever 6.08 -fee salary 3.72 -nerve brain 3.88 -beast animal 7.83 -dinner chicken 2.85 -girl maid 2.93 -child boy 5.75 -alcohol wine 7.42 -nose mouth 3.73 -street car 2.38 -bell door 2.2 -box hat 1.3 -belief impression 5.95 -bias opinion 5.6 -attention awareness 8.73 -anger mood 4.1 -elegance style 5.72 -beauty age 1.58 -book theme 2.58 -friend mother 2.53 -vitamin iron 5.55 -car factory 2.75 -pact condition 2.45 -chapter choice 0.48 -arithmetic rhythm 2.35 -winner presence 1.08 -belief flower 0.4 -winner goal 3.23 -trick size 0.48 -choice vein 0.98 -hymn conquest 0.68 -endurance band 0.4 -flower endurance 0.4 -hole agreement 0.3 -doctor temper 0.48 -fraternity door 0.68 -task woman 0.68 -fraternity baseball 0.88 -cent size 0.4 -presence door 0.48 -mouse management 0.48 -liquor century 0.4 -task straw 0.68 -night chapter 0.48 -pollution president 0.68 -gun trick 0.48 -bath trick 0.58 -diet apple 1.18 -chapter tail 0.3 -course stomach 0.58 -hymn straw 0.4 -dentist colonel 0.4 -wife straw 0.4 -hole wife 0.68 -pupil president 0.78 -bath wife 0.48 -people cent 0.48 -formula log 1.77 -woman fur 0.58 -apple sunshine 0.58 -gun dawn 1.18 -meal waist 0.98 -camera president 0.48 -liquor band 0.68 -stomach vein 2.35 -gun fur 0.3 -couch baseball 0.88 -worker camera 0.68 -deck mouse 0.48 -rice boy 0.4 -people gun 0.68 -cliff tail 0.3 -ankle window 0.3 -princess island 0.3 -container mouse 0.3 -wagon container 2.65 -dollar people 0.4 -bath balloon 0.4 -stomach bedroom 0.4 -bicycle bedroom 0.4 -log bath 0.4 -bowl tail 0.48 diff --git a/sematch/eval/word_similarity/graph_ws353-sim.txt b/sematch/eval/word_similarity/graph_ws353-sim.txt deleted file mode 100644 index 97d84ca..0000000 --- a/sematch/eval/word_similarity/graph_ws353-sim.txt +++ /dev/null @@ -1,189 +0,0 @@ -tiger cat 7.35 -tiger tiger 10.0 -plane car 5.77 -train car 6.31 -television radio 6.77 -media radio 7.42 -bread butter 6.19 -cucumber potato 5.92 -doctor nurse 7.0 -professor doctor 6.62 -student professor 6.81 -wood forest 7.73 -money cash 9.15 -king queen 8.58 -king rook 5.92 -bishop rabbi 6.69 -fuck sex 9.44 -football soccer 9.03 -football basketball 6.81 -football tennis 6.63 -Arafat Jackson 2.5 -physics chemistry 7.35 -vodka gin 8.46 -vodka brandy 8.13 -car automobile 8.94 -gem jewel 8.96 -journey voyage 9.29 -boy lad 8.83 -coast shore 9.1 -asylum madhouse 8.87 -magician wizard 9.02 -furnace stove 8.79 -food fruit 7.52 -bird cock 7.1 -bird crane 7.38 -food rooster 4.42 -money dollar 8.42 -money currency 9.04 -tiger jaguar 8.0 -tiger feline 8.0 -tiger carnivore 7.08 -tiger mammal 6.85 -tiger animal 7.0 -tiger organism 4.77 -tiger fauna 5.62 -psychology psychiatry 8.08 -psychology science 6.71 -psychology discipline 5.58 -planet star 8.45 -planet moon 8.08 -planet sun 8.02 -precedent example 5.85 -precedent antecedent 6.04 -cup tableware 6.85 -cup artifact 2.92 -cup object 3.69 -jaguar cat 7.42 -jaguar car 7.27 -mile kilometer 8.66 -skin eye 6.22 -Japanese American 6.5 -century year 7.59 -announcement news 7.56 -doctor personnel 5.0 -Harvard Yale 8.13 -hospital infrastructure 4.63 -life death 7.88 -travel activity 5.0 -type kind 8.97 -street place 6.44 -street avenue 8.88 -street block 6.88 -cell phone 7.81 -dividend payment 7.63 -calculation computation 8.44 -profit loss 7.63 -dollar yen 7.78 -dollar buck 9.22 -phone equipment 7.13 -liquid water 7.89 -marathon sprint 7.47 -seafood food 8.34 -seafood lobster 8.7 -lobster food 7.81 -lobster wine 5.7 -championship tournament 8.36 -man woman 8.3 -man governor 5.25 -murder manslaughter 8.53 -opera performance 6.88 -Mexico Brazil 7.44 -glass metal 5.56 -aluminum metal 7.83 -rock jazz 7.59 -museum theater 7.19 -shower thunderstorm 6.31 -monk oracle 5.0 -cup food 5.0 -journal association 4.97 -street children 4.94 -car flight 4.94 -space chemistry 4.88 -situation conclusion 4.81 -word similarity 4.75 -peace plan 4.75 -consumer energy 4.75 -ministry culture 4.69 -investigation effort 4.59 -image surface 4.56 -life term 4.5 -start match 4.47 -board recommendation 4.47 -lad brother 4.46 -observation architecture 4.38 -coast hill 4.38 -deployment departure 4.25 -benchmark index 4.25 -attempt peace 4.25 -start year 4.06 -focus life 4.06 -development issue 3.97 -theater history 3.91 -situation isolation 3.88 -profit warning 3.88 -media trading 3.88 -chance credibility 3.88 -precedent information 3.85 -architecture century 3.78 -population development 3.75 -peace atmosphere 3.69 -morality marriage 3.69 -minority peace 3.69 -atmosphere landscape 3.69 -report gain 3.63 -music project 3.63 -seven series 3.56 -experience music 3.47 -school center 3.44 -five month 3.38 -announcement production 3.38 -morality importance 3.31 -money operation 3.31 -delay news 3.31 -practice institution 3.19 -century nation 3.16 -coast forest 3.15 -shore woodland 3.08 -drink car 3.04 -president medal 3.0 -prejudice recognition 3.0 -viewer serial 2.97 -peace insurance 2.94 -Mars water 2.94 -media gain 2.88 -precedent cognition 2.81 -announcement effort 2.75 -line insurance 2.69 -crane implement 2.69 -drink mother 2.65 -opera industry 2.63 -listing proximity 2.56 -precedent collection 2.5 -cup article 2.4 -sign recess 2.38 -reason hypertension 2.31 -direction combination 2.25 -Wednesday news 2.22 -glass magician 2.08 -cemetery woodland 2.08 -cup substance 1.92 -forest graveyard 1.85 -stock egg 1.81 -energy secretary 1.81 -precedent group 1.77 -production hike 1.75 -stock phone 1.62 -holy sex 1.62 -stock CD 1.31 -drink ear 1.31 -delay racism 1.19 -stock life 0.92 -stock jaguar 0.92 -monk slave 0.92 -lad wizard 0.92 -sugar approach 0.88 -noon string 0.54 -chord smile 0.54 -professor cucumber 0.31 -king cabbage 0.23 diff --git a/sematch/eval/word_similarity/graph_ws353.txt b/sematch/eval/word_similarity/graph_ws353.txt deleted file mode 100644 index bc5869c..0000000 --- a/sematch/eval/word_similarity/graph_ws353.txt +++ /dev/null @@ -1,321 +0,0 @@ -love sex 6.77 -tiger cat 7.35 -tiger tiger 10.0 -book paper 7.46 -computer keyboard 7.62 -computer internet 7.58 -plane car 5.77 -train car 6.31 -television radio 6.77 -media radio 7.42 -bread butter 6.19 -cucumber potato 5.92 -doctor nurse 7.0 -professor doctor 6.62 -student professor 6.81 -company stock 7.08 -stock market 8.08 -stock phone 1.62 -stock CD 1.31 -stock jaguar 0.92 -stock egg 1.81 -stock life 0.92 -book library 7.46 -bank money 8.12 -wood forest 7.73 -money cash 9.15 -professor cucumber 0.31 -king cabbage 0.23 -king queen 8.58 -king rook 5.92 -bishop rabbi 6.69 -Jerusalem Israel 8.46 -Jerusalem Palestinian 7.65 -holy sex 1.62 -fuck sex 9.44 -football soccer 9.03 -football basketball 6.81 -football tennis 6.63 -tennis racket 7.56 -Arafat terror 7.65 -Arafat Jackson 2.5 -movie star 7.38 -movie popcorn 6.19 -movie critic 6.73 -movie theater 7.92 -physics proton 8.12 -physics chemistry 7.35 -space chemistry 4.88 -alcohol chemistry 5.54 -vodka gin 8.46 -vodka brandy 8.13 -drink car 3.04 -drink ear 1.31 -drink mouth 5.96 -baby mother 7.85 -drink mother 2.65 -car automobile 8.94 -gem jewel 8.96 -journey voyage 9.29 -boy lad 8.83 -coast shore 9.1 -asylum madhouse 8.87 -magician wizard 9.02 -furnace stove 8.79 -food fruit 7.52 -bird cock 7.1 -bird crane 7.38 -tool implement 6.46 -brother monk 6.27 -crane implement 2.69 -lad brother 4.46 -monk oracle 5.0 -cemetery woodland 2.08 -food rooster 4.42 -coast hill 4.38 -forest graveyard 1.85 -shore woodland 3.08 -monk slave 0.92 -coast forest 3.15 -lad wizard 0.92 -chord smile 0.54 -glass magician 2.08 -noon string 0.54 -money dollar 8.42 -money cash 9.08 -money currency 9.04 -money wealth 8.27 -money property 7.57 -money possession 7.29 -money bank 8.5 -money deposit 7.73 -money withdrawal 6.88 -money laundering 5.65 -money operation 3.31 -tiger jaguar 8.0 -tiger feline 8.0 -tiger carnivore 7.08 -tiger mammal 6.85 -tiger animal 7.0 -tiger organism 4.77 -tiger fauna 5.62 -tiger zoo 5.87 -psychology psychiatry 8.08 -psychology anxiety 7.0 -psychology fear 6.85 -psychology depression 7.42 -psychology clinic 6.58 -psychology doctor 6.42 -psychology mind 7.69 -psychology health 7.23 -psychology science 6.71 -psychology discipline 5.58 -psychology cognition 7.48 -planet star 8.45 -planet constellation 8.06 -planet moon 8.08 -planet sun 8.02 -planet galaxy 8.11 -planet space 7.92 -planet astronomer 7.94 -precedent example 5.85 -precedent information 3.85 -precedent cognition 2.81 -precedent law 6.65 -precedent collection 2.5 -precedent group 1.77 -precedent antecedent 6.04 -cup coffee 6.58 -cup tableware 6.85 -cup article 2.4 -cup artifact 2.92 -cup object 3.69 -cup drink 7.25 -cup food 5.0 -cup substance 1.92 -cup liquid 5.9 -jaguar cat 7.42 -jaguar car 7.27 -energy secretary 1.81 -energy laboratory 5.09 -computer laboratory 6.78 -weapon secret 6.06 -FBI fingerprint 6.94 -FBI investigation 8.31 -investigation effort 4.59 -Mars water 2.94 -Mars scientist 5.63 -news report 8.16 -canyon landscape 7.53 -image surface 4.56 -discovery space 6.34 -water seepage 6.56 -sign recess 2.38 -Wednesday news 2.22 -mile kilometer 8.66 -territory surface 5.34 -atmosphere landscape 3.69 -president medal 3.0 -war troops 8.13 -record number 6.31 -skin eye 6.22 -Japanese American 6.5 -theater history 3.91 -prejudice recognition 3.0 -decoration valor 5.63 -century year 7.59 -century nation 3.16 -delay racism 1.19 -delay news 3.31 -minister party 6.63 -peace plan 4.75 -minority peace 3.69 -attempt peace 4.25 -government crisis 6.56 -deployment departure 4.25 -deployment withdrawal 5.88 -energy crisis 5.94 -announcement news 7.56 -announcement effort 2.75 -stroke hospital 7.03 -disability death 5.47 -victim emergency 6.47 -treatment recovery 7.91 -journal association 4.97 -doctor personnel 5.0 -doctor liability 5.19 -liability insurance 7.03 -school center 3.44 -reason hypertension 2.31 -reason criterion 5.91 -hundred percent 7.38 -Harvard Yale 8.13 -hospital infrastructure 4.63 -death row 5.25 -death inmate 5.03 -life death 7.88 -life term 4.5 -word similarity 4.75 -board recommendation 4.47 -OPEC country 5.63 -peace atmosphere 3.69 -peace insurance 2.94 -territory kilometer 5.28 -travel activity 5.0 -competition price 6.44 -consumer energy 4.75 -car flight 4.94 -credit card 8.06 -credit information 5.31 -hotel reservation 8.03 -registration arrangement 6.0 -arrangement accommodation 5.41 -type kind 8.97 -arrival hotel 6.0 -bed closet 6.72 -closet clothes 8.0 -situation conclusion 4.81 -situation isolation 3.88 -impartiality interest 5.16 -direction combination 2.25 -street place 6.44 -street avenue 8.88 -street block 6.88 -street children 4.94 -listing proximity 2.56 -listing category 6.38 -cell phone 7.81 -production hike 1.75 -benchmark index 4.25 -media trading 3.88 -media gain 2.88 -dividend payment 7.63 -dividend calculation 6.48 -calculation computation 8.44 -currency market 7.5 -OPEC oil 8.59 -oil stock 6.34 -announcement production 3.38 -announcement warning 6.0 -profit warning 3.88 -profit loss 7.63 -dollar yen 7.78 -dollar buck 9.22 -dollar profit 7.38 -dollar loss 6.09 -network hardware 8.31 -phone equipment 7.13 -equipment maker 5.91 -five month 3.38 -report gain 3.63 -liquid water 7.89 -baseball season 5.97 -game victory 7.03 -game team 7.69 -marathon sprint 7.47 -game series 6.19 -game defeat 6.97 -seven series 3.56 -seafood sea 7.47 -seafood food 8.34 -seafood lobster 8.7 -lobster food 7.81 -lobster wine 5.7 -food preparation 6.22 -video archive 6.34 -start year 4.06 -start match 4.47 -game round 5.97 -boxing round 7.61 -championship tournament 8.36 -line insurance 2.69 -day summer 3.94 -summer drought 7.16 -summer nature 5.63 -day dawn 7.53 -nature environment 8.31 -environment ecology 8.81 -nature man 6.25 -man woman 8.3 -man governor 5.25 -murder manslaughter 8.53 -soap opera 7.94 -opera performance 6.88 -life lesson 5.94 -focus life 4.06 -production crew 6.25 -television film 7.72 -lover quarrel 6.19 -viewer serial 2.97 -population development 3.75 -morality importance 3.31 -morality marriage 3.69 -Mexico Brazil 7.44 -gender equality 6.41 -change attitude 5.44 -family planning 6.25 -opera industry 2.63 -sugar approach 0.88 -practice institution 3.19 -ministry culture 4.69 -problem challenge 6.75 -size prominence 5.31 -country citizen 7.31 -development issue 3.97 -experience music 3.47 -music project 3.63 -glass metal 5.56 -aluminum metal 7.83 -chance credibility 3.88 -exhibit memorabilia 5.31 -rock jazz 7.59 -museum theater 7.19 -observation architecture 4.38 -space world 6.53 -preservation world 6.19 -admission ticket 7.69 -shower thunderstorm 6.31 -shower flood 6.03 -disaster area 6.25 -governor office 6.34 -architecture century 3.78 diff --git a/sematch/eval/word_similarity/noun_mc.txt b/sematch/eval/word_similarity/noun_mc.txt deleted file mode 100644 index 4190c1f..0000000 --- a/sematch/eval/word_similarity/noun_mc.txt +++ /dev/null @@ -1,30 +0,0 @@ -car automobile 3.92 -gem jewel 3.84 -journey voyage 3.84 -boy lad 3.76 -coast shore 3.70 -asylum madhouse 3.61 -magician wizard 3.50 -midday noon 3.42 -furnace stove 3.11 -food fruit 3.08 -bird cock 3.05 -bird crane 2.97 -tool implement 2.95 -brother monk 2.82 -lad brother 1.66 -crane implement 1.68 -journey car 1.16 -monk oracle 1.10 -cemetery woodland 0.95 -food rooster 0.89 -coast hill 0.87 -forest graveyard 0.84 -shore woodland 0.63 -monk slave 0.55 -coast forest 0.42 -lad wizard 0.42 -chord smile 0.13 -glass magician 0.11 -rooster voyage 0.08 -noon string 0.008 \ No newline at end of file diff --git a/sematch/eval/word_similarity/noun_rg.txt b/sematch/eval/word_similarity/noun_rg.txt deleted file mode 100644 index 7a4b5de..0000000 --- a/sematch/eval/word_similarity/noun_rg.txt +++ /dev/null @@ -1,65 +0,0 @@ -gem jewel 3.94 -midday noon 3.94 -automobile car 3.92 -cemetery graveyard 3.88 -cushion pillow 3.84 -boy lad 3.82 -cock rooster 3.68 -implement tool 3.66 -forest woodland 3.65 -coast shore 3.60 -autograph signature 3.59 -journey voyage 3.58 -serf slave 3.46 -grin smile 3.46 -glass tumbler 3.45 -cord string 3.41 -hill mound 3.29 -magician wizard 3.21 -furnace stove 3.11 -asylum madhouse 3.04 -brother monk 2.74 -food fruit 2.69 -bird cock 2.63 -bird crane 2.63 -oracle sage 2.61 -sage wizard 2.46 -brother lad 2.41 -crane implement 2.37 -magician oracle 1.82 -glass jewel 1.78 -cemetery mound 1.69 -car journey 1.55 -hill woodland 1.48 -crane rooster 1.41 -furnace implement 1.37 -coast hill 1.26 -bird woodland 1.24 -shore voyage 1.22 -cemetery woodland 1.18 -food rooster 1.09 -forest graveyard 1.00 -lad wizard 0.99 -mound shore 0.97 -automobile cushion 0.97 -boy sage 0.96 -monk oracle 0.91 -shore woodland 0.90 -grin lad 0.88 -coast forest 0.85 -asylum cemetery 0.79 -monk slave 0.57 -cushion jewel 0.45 -boy rooster 0.44 -glass magician 0.44 -graveyard madhouse 0.44 -asylum monk 0.39 -asylum fruit 0.19 -grin implement 0.18 -mound stove 0.14 -automobile wizard 0.11 -autograph shore 0.06 -fruit furnace 0.05 -noon string 0.04 -rooster voyage 0.04 -cord smile 0.02 \ No newline at end of file diff --git a/sematch/eval/word_similarity/noun_simlex.txt b/sematch/eval/word_similarity/noun_simlex.txt deleted file mode 100644 index 5dcdbce..0000000 --- a/sematch/eval/word_similarity/noun_simlex.txt +++ /dev/null @@ -1,666 +0,0 @@ -wife husband 2.3 -book text 6.35 -groom bride 3.17 -night day 1.88 -south north 2.2 -plane airport 3.65 -uncle aunt 5.5 -horse mare 8.33 -bottom top 0.7 -friend buddy 8.78 -student pupil 9.35 -world globe 6.67 -leg arm 2.88 -plane jet 8.1 -woman man 3.33 -horse colt 7.07 -actress actor 7.12 -teacher instructor 9.25 -movie film 8.87 -bird hawk 7.85 -word dictionary 3.68 -money salary 7.88 -dog cat 1.75 -area region 9.47 -navy army 6.43 -book literature 7.53 -clothes closet 3.27 -sunset sunrise 2.47 -child adult 2.98 -cow cattle 9.52 -book story 5.63 -winter summer 2.38 -taxi cab 9.2 -tree maple 5.53 -bed bedroom 3.4 -roof ceiling 7.58 -disease infection 7.15 -arm shoulder 4.85 -sheep lamb 8.42 -lady gentleman 3.42 -boat anchor 2.25 -priest monk 6.28 -toe finger 4.68 -river stream 7.3 -anger fury 8.73 -date calendar 4.42 -sea ocean 8.27 -second minute 4.62 -hand thumb 3.88 -wood log 7.3 -mud dirt 7.32 -hallway corridor 9.28 -way manner 7.62 -mouse cat 1.12 -cop sheriff 9.05 -death burial 4.93 -music melody 6.98 -beer alcohol 7.5 -mouth lip 7.1 -storm hurricane 6.38 -tax income 2.38 -flower violet 6.95 -paper cardboard 5.38 -floor ceiling 1.73 -beach seashore 8.33 -rod curtain 3.03 -hound fox 2.38 -street alley 5.48 -boat deck 4.28 -car horn 2.57 -friend guest 4.25 -employer employee 3.65 -hand wrist 3.97 -ball cannon 2.58 -alcohol brandy 6.98 -victory triumph 8.98 -telephone booth 3.63 -door doorway 5.4 -motel inn 8.17 -clothes cloth 5.47 -steak meat 7.47 -nail thumb 3.55 -band orchestra 7.08 -book bible 5 -business industry 7.02 -winter season 6.27 -decade century 3.48 -alcohol gin 8.65 -hat coat 2.67 -window door 3.33 -arm wrist 3.57 -house apartment 5.8 -glass crystal 6.27 -wine brandy 5.15 -creator maker 9.62 -dinner breakfast 3.33 -arm muscle 3.72 -bubble suds 8.57 -bread flour 3.33 -death tragedy 5.8 -absence presence 0.4 -gun cannon 5.68 -grass blade 4.57 -ball basket 1.67 -hose garden 1.67 -boy kid 7.5 -church choir 2.95 -clothes drawer 3.02 -tower bell 1.9 -father parent 7.07 -school grade 4.42 -parent adult 5.37 -bar jail 1.9 -car highway 3.4 -dictionary definition 6.25 -door cellar 1.97 -army legion 5.95 -metal aluminum 7.25 -chair bench 6.67 -cloud fog 6 -boy son 6.75 -water ice 6.47 -bed blanket 3.02 -attorney lawyer 9.35 -area zone 8.33 -business company 9.02 -clothes fabric 5.87 -sweater jacket 7.15 -money capital 6.67 -hand foot 4.17 -alcohol cocktail 6.73 -yard inch 3.78 -molecule atom 6.45 -lens camera 4.28 -meal dinner 7.15 -eye tear 3.55 -god devil 1.8 -loop belt 3.1 -rat mouse 7.78 -motor engine 8.65 -car cab 7.42 -cat lion 6.75 -size magnitude 6.33 -reality fantasy 1.03 -door gate 5.25 -cat pet 5.95 -tin aluminum 6.42 -bone jaw 4.17 -cereal wheat 3.75 -house key 1.9 -blood flesh 4.28 -door corridor 3.73 -god spirit 7.3 -capability competence 7.62 -abundance plenty 8.97 -sofa chair 6.67 -wall brick 4.68 -horn drum 2.68 -organ liver 6.15 -strength might 7.07 -phrase word 5.48 -band parade 3.92 -stomach waist 5.9 -cloud storm 5.6 -joy pride 5 -noise rattle 6.17 -rain mist 5.97 -beer beverage 5.42 -man uncle 3.92 -apple juice 2.88 -intelligence logic 6.5 -communication language 7.47 -mink fur 6.83 -mob crowd 7.85 -shore coast 8.83 -wire cord 7.62 -bird turkey 6.58 -bed crib 7.3 -competence ability 7.5 -cloud haze 7.32 -supper meal 7.53 -bar cage 2.8 -water salt 1.3 -sense intuition 7.68 -situation condition 6.58 -crime theft 7.53 -style fashion 8.5 -boundary border 9.08 -arm body 4.05 -boat car 2.37 -sandwich lunch 6.3 -bride princess 2.8 -heroine hero 8.78 -car gauge 1.13 -insect bee 6.07 -crib cradle 8.55 -animal person 3.05 -marijuana herb 6.5 -bed hospital 0.92 -cheek tongue 4.52 -disc computer 3.2 -curve angle 3.33 -grass moss 5 -school law 1.13 -foot head 2.3 -mother guardian 6.5 -orthodontist dentist 8.27 -alcohol whiskey 7.27 -mouth tooth 6.3 -breakfast bacon 4.37 -bathroom bedroom 3.4 -plate bowl 5.23 -meat bacon 5.8 -air helium 3.63 -worker employer 5.37 -body chest 4.45 -son father 3.82 -heart surgery 1.08 -woman secretary 1.98 -man father 4.83 -beach island 5.6 -story topic 5 -game fun 3.42 -weekend week 4 -couple pair 8.33 -woman wife 5.72 -sheep cattle 4.77 -purse bag 8.33 -ceiling cathedral 2.42 -bean coffee 5.15 -wood paper 2.88 -top side 1.9 -crime fraud 5.65 -pain harm 5.38 -lover companion 5.97 -evening dusk 7.78 -father daughter 2.62 -wine liquor 7.85 -cow goat 2.93 -belief opinion 7.7 -reality illusion 1.42 -pact agreement 9.02 -wealth poverty 1.27 -accident emergency 4.93 -battle conquest 7.22 -friend teacher 2.62 -illness infection 6.9 -game trick 2.32 -brother son 3.48 -aunt nephew 3.1 -worker mechanic 4.92 -doctor orthodontist 5.58 -oak maple 6.03 -bee queen 3.27 -car bicycle 3.47 -goal quest 5.83 -august month 5.53 -army squad 5.08 -cloud weather 4.87 -physician doctor 8.88 -canyon valley 6.75 -river valley 1.67 -sun sky 2.27 -target arrow 3.25 -chocolate pie 2.27 -circumstance situation 7.85 -opinion choice 5.43 -rhythm melody 6.12 -gut nerve 4.93 -day dawn 5.47 -cattle beef 7.03 -doctor professor 4.65 -arm vein 3.65 -room bath 3.33 -corporation business 9.02 -fun football 1.97 -hill cliff 4.28 -bone ankle 3.82 -apple candy 2.08 -helper maid 5.58 -leader manager 7.27 -lemon tea 1.6 -bee ant 2.78 -basketball baseball 4.92 -rice bean 2.72 -bed furniture 6.08 -emotion passion 7.72 -anarchy chaos 7.93 -crime violation 7.12 -machine engine 5.58 -beach sea 4.68 -alley bowl 1.53 -jar bottle 7.83 -strength capability 5.28 -seed mustard 3.48 -guitar drum 3.78 -opinion idea 5.7 -north west 3.63 -diet salad 2.98 -mother wife 3.02 -dad mother 3.55 -captain sailor 5 -meter yard 5.6 -beer champagne 4.45 -motor boat 2.57 -card bridge 1.97 -science psychology 4.92 -sinner saint 1.6 -destruction construction 0.98 -crowd bunch 7.42 -beach reef 3.77 -man child 4.13 -bread cheese 1.95 -champion winner 8.73 -celebration ceremony 7.72 -menu order 3.62 -king princess 3.27 -wealth prestige 6.07 -endurance strength 6.58 -danger threat 8.78 -god priest 4.5 -men fraternity 3.13 -buddy companion 8.65 -teacher helper 4.28 -body stomach 3.93 -tongue throat 3.1 -house carpet 1.38 -intelligence skill 5.35 -journey conquest 4.72 -god prey 1.23 -brother soul 0.97 -adversary opponent 9.05 -death catastrophe 4.13 -monster demon 6.95 -day morning 4.87 -man victor 1.9 -friend guy 3.88 -song story 3.97 -ray sunshine 6.83 -guy stud 5.83 -chicken rice 1.43 -box elevator 1.32 -butter potato 1.22 -apartment furniture 1.28 -lake swamp 4.92 -salad vinegar 1.13 -flower bulb 4.48 -cloud mist 6.67 -driver pilot 6.28 -sugar honey 5.13 -body shoulder 2.88 -idea image 3.55 -father brother 4.2 -moon planet 5.87 -ball costume 2.32 -rail fence 5.22 -room bed 2.35 -flower bush 4.25 -bone knee 4.17 -arm knee 2.75 -bottom side 2.63 -vessel vein 5.15 -cat rabbit 2.37 -meat sandwich 2.35 -belief concept 5.08 -intelligence insight 5.9 -attention interest 7.22 -attitude confidence 4.35 -right justice 7.05 -argument agreement 1.45 -depth magnitude 6.12 -medium news 3.65 -winner candidate 2.78 -birthday date 5.08 -fee payment 7.15 -bible hymn 5.15 -exit doorway 5.5 -man sentry 3.25 -aisle hall 6.35 -whiskey gin 6.28 -blood marrow 3.4 -oil mink 1.23 -floor deck 5.55 -roof floor 2.62 -door floor 1.67 -shoulder head 3.42 -wagon carriage 7.7 -car carriage 5.13 -elbow ankle 3.13 -wealth fame 4.02 -sorrow shame 4.77 -administration management 7.25 -communication conversation 8.02 -pollution atmosphere 4.25 -anatomy biology 5.33 -college profession 3.12 -book topic 2.07 -formula equation 7.95 -book information 5 -boy partner 1.9 -sky universe 4.68 -population people 7.68 -college class 4.13 -chief mayor 4.85 -rabbi minister 7.62 -meter inch 5.08 -polyester cotton 5.63 -lawyer banker 1.88 -violin instrument 6.58 -camp cabin 4.2 -pot appliance 2.53 -linen fabric 7.47 -whiskey champagne 5.33 -girl child 5.38 -cottage cabin 7.72 -bird hen 7.03 -racket noise 8.1 -sunset evening 5.98 -drizzle rain 9.17 -adult baby 2.22 -charcoal coal 7.63 -body spine 4.78 -head nail 2.47 -log timber 8.05 -spoon cup 2.02 -body nerve 3.13 -man husband 5.32 -bone neck 2.53 -frustration anger 6.5 -river sea 5.72 -task job 8.87 -club society 5.23 -reflection image 7.27 -prince king 5.92 -snow weather 5.48 -people party 2.2 -boy brother 6.67 -root grass 3.55 -brow eye 3.82 -money pearl 2.1 -money diamond 3.42 -vehicle bus 6.47 -cab bus 5.6 -house barn 4.33 -finger palm 3.33 -car bridge 0.95 -effort difficulty 4.45 -fact insight 4.77 -job management 3.97 -cancer sickness 7.93 -word newspaper 2.47 -composer writer 6.58 -actor singer 4.52 -shelter hut 6.47 -bathroom kitchen 3.1 -cabin hut 6.53 -door kitchen 1.67 -value belief 7.07 -wisdom intelligence 7.47 -ignorance intelligence 1.5 -happiness luck 2.38 -idea scheme 6.75 -mood emotion 8.12 -happiness peace 6.03 -despair misery 7.22 -logic arithmetic 3.97 -denial confession 1.03 -argument criticism 5.08 -aggression hostility 8.48 -hysteria confusion 6.33 -chemistry theory 3.17 -trial verdict 3.33 -comfort safety 5.8 -confidence self 3.12 -vision perception 6.88 -era decade 5.4 -biography fiction 1.38 -discussion argument 5.48 -code symbol 6.03 -danger disease 3 -accident catastrophe 5.9 -journey trip 8.88 -activity movement 7.15 -gossip news 5.22 -father god 3.57 -action course 5.45 -fever illness 7.65 -aviation flight 8.18 -game action 4.85 -molecule air 3.05 -home state 2.58 -word literature 4.77 -adult guardian 6.9 -newspaper information 5.65 -communication television 5.6 -cousin uncle 4.63 -author reader 1.6 -guy partner 3.57 -area corner 2.07 -ballad song 7.53 -wall decoration 2.62 -word page 2.92 -nurse scientist 2.08 -politician president 7.38 -president mayor 5.68 -book essay 4.72 -man warrior 4.72 -article journal 6.18 -breakfast supper 4.4 -crowd parade 3.93 -aisle hallway 6.75 -teacher rabbi 4.37 -hip lip 1.43 -book article 5.43 -room cell 4.58 -box booth 3.8 -daughter kid 4.17 -limb leg 6.9 -liver lung 2.7 -classroom hallway 2 -mountain ledge 3.73 -car elevator 1.03 -bed couch 3.42 -clothes button 2.3 -clothes coat 5.35 -kidney organ 6.17 -apple sauce 1.43 -chicken steak 3.73 -car hose 0.87 -tobacco cigarette 7.5 -student professor 1.95 -baby daughter 5 -pipe cigar 6.03 -milk juice 4.05 -box cigar 1.25 -apartment hotel 3.33 -cup cone 3.17 -horse ox 3.02 -throat nose 2.8 -bone teeth 4.17 -bone elbow 3.78 -bacon bean 1.22 -cup jar 5.13 -proof fact 7.3 -appointment engagement 6.75 -birthday year 1.67 -word clue 2.53 -author creator 8.02 -atom carbon 3.1 -archbishop bishop 7.05 -letter paragraph 4 -page paragraph 3.03 -steeple chapel 7.08 -muscle bone 3.65 -muscle tongue 5 -boy soldier 2.15 -belly abdomen 8.13 -guy girl 3.33 -bed chair 3.5 -clothes jacket 5.15 -gun knife 3.65 -tin metal 5.63 -bottle container 7.93 -hen turkey 6.13 -meat bread 1.67 -arm bone 3.83 -neck spine 5.32 -apple lemon 4.05 -agony grief 7.63 -assignment task 8.7 -night dawn 2.95 -dinner soup 3.72 -calf bull 4.93 -snow storm 4.8 -nail hand 3.42 -dog horse 2.38 -arm neck 1.58 -ball glove 1.75 -flu fever 6.08 -fee salary 3.72 -nerve brain 3.88 -beast animal 7.83 -dinner chicken 2.85 -girl maid 2.93 -child boy 5.75 -alcohol wine 7.42 -nose mouth 3.73 -street car 2.38 -bell door 2.2 -box hat 1.3 -belief impression 5.95 -bias opinion 5.6 -attention awareness 8.73 -anger mood 4.1 -elegance style 5.72 -beauty age 1.58 -book theme 2.58 -friend mother 2.53 -vitamin iron 5.55 -car factory 2.75 -pact condition 2.45 -chapter choice 0.48 -arithmetic rhythm 2.35 -winner presence 1.08 -belief flower 0.4 -winner goal 3.23 -trick size 0.48 -choice vein 0.98 -hymn conquest 0.68 -endurance band 0.4 -jail choice 1.08 -condition boy 0.48 -flower endurance 0.4 -hole agreement 0.3 -doctor temper 0.48 -fraternity door 0.68 -task woman 0.68 -fraternity baseball 0.88 -cent size 0.4 -presence door 0.48 -mouse management 0.48 -task highway 0.48 -liquor century 0.4 -task straw 0.68 -island task 0.3 -night chapter 0.48 -pollution president 0.68 -gun trick 0.48 -bath trick 0.58 -diet apple 1.18 -cent wife 0.58 -chapter tail 0.3 -course stomach 0.58 -hymn straw 0.4 -dentist colonel 0.4 -wife straw 0.4 -hole wife 0.68 -pupil president 0.78 -bath wife 0.48 -people cent 0.48 -formula log 1.77 -woman fur 0.58 -apple sunshine 0.58 -gun dawn 1.18 -meal waist 0.98 -camera president 0.48 -liquor band 0.68 -stomach vein 2.35 -gun fur 0.3 -couch baseball 0.88 -worker camera 0.68 -deck mouse 0.48 -rice boy 0.4 -people gun 0.68 -cliff tail 0.3 -ankle window 0.3 -princess island 0.3 -container mouse 0.3 -wagon container 2.65 -people balloon 0.48 -dollar people 0.4 -bath balloon 0.4 -stomach bedroom 0.4 -bicycle bedroom 0.4 -log bath 0.4 -bowl tail 0.48 diff --git a/sematch/eval/word_similarity/noun_ws353-sim.txt b/sematch/eval/word_similarity/noun_ws353-sim.txt deleted file mode 100644 index 4af6584..0000000 --- a/sematch/eval/word_similarity/noun_ws353-sim.txt +++ /dev/null @@ -1,201 +0,0 @@ -tiger cat 7.35 -tiger tiger 10.0 -plane car 5.77 -train car 6.31 -television radio 6.77 -media radio 7.42 -bread butter 6.19 -cucumber potato 5.92 -doctor nurse 7.0 -professor doctor 6.62 -student professor 6.81 -smart stupid 5.81 -wood forest 7.73 -money cash 9.15 -king queen 8.58 -king rook 5.92 -bishop rabbi 6.69 -fuck sex 9.44 -football soccer 9.03 -football basketball 6.81 -football tennis 6.63 -Arafat Jackson 2.5 -physics chemistry 7.35 -vodka gin 8.46 -vodka brandy 8.13 -car automobile 8.94 -gem jewel 8.96 -journey voyage 9.29 -boy lad 8.83 -coast shore 9.1 -asylum madhouse 8.87 -magician wizard 9.02 -midday noon 9.29 -furnace stove 8.79 -food fruit 7.52 -bird cock 7.1 -bird crane 7.38 -food rooster 4.42 -money dollar 8.42 -money currency 9.04 -tiger jaguar 8.0 -tiger feline 8.0 -tiger carnivore 7.08 -tiger mammal 6.85 -tiger animal 7.0 -tiger organism 4.77 -tiger fauna 5.62 -psychology psychiatry 8.08 -psychology science 6.71 -psychology discipline 5.58 -planet star 8.45 -planet moon 8.08 -planet sun 8.02 -precedent example 5.85 -precedent antecedent 6.04 -cup tableware 6.85 -cup artifact 2.92 -cup object 3.69 -cup entity 2.15 -jaguar cat 7.42 -jaguar car 7.27 -mile kilometer 8.66 -skin eye 6.22 -Japanese American 6.5 -century year 7.59 -announcement news 7.56 -doctor personnel 5.0 -Harvard Yale 8.13 -hospital infrastructure 4.63 -life death 7.88 -travel activity 5.0 -type kind 8.97 -street place 6.44 -street avenue 8.88 -street block 6.88 -cell phone 7.81 -dividend payment 7.63 -calculation computation 8.44 -profit loss 7.63 -dollar yen 7.78 -dollar buck 9.22 -phone equipment 7.13 -liquid water 7.89 -marathon sprint 7.47 -seafood food 8.34 -seafood lobster 8.7 -lobster food 7.81 -lobster wine 5.7 -championship tournament 8.36 -man woman 8.3 -man governor 5.25 -murder manslaughter 8.53 -opera performance 6.88 -Mexico Brazil 7.44 -glass metal 5.56 -aluminum metal 7.83 -rock jazz 7.59 -museum theater 7.19 -shower thunderstorm 6.31 -monk oracle 5.0 -cup food 5.0 -journal association 4.97 -street children 4.94 -car flight 4.94 -space chemistry 4.88 -situation conclusion 4.81 -word similarity 4.75 -peace plan 4.75 -consumer energy 4.75 -ministry culture 4.69 -smart student 4.62 -investigation effort 4.59 -image surface 4.56 -life term 4.5 -start match 4.47 -computer news 4.47 -board recommendation 4.47 -lad brother 4.46 -observation architecture 4.38 -coast hill 4.38 -deployment departure 4.25 -benchmark index 4.25 -attempt peace 4.25 -consumer confidence 4.13 -start year 4.06 -focus life 4.06 -development issue 3.97 -theater history 3.91 -situation isolation 3.88 -profit warning 3.88 -media trading 3.88 -chance credibility 3.88 -precedent information 3.85 -architecture century 3.78 -population development 3.75 -peace atmosphere 3.69 -morality marriage 3.69 -minority peace 3.69 -atmosphere landscape 3.69 -report gain 3.63 -music project 3.63 -seven series 3.56 -experience music 3.47 -school center 3.44 -five month 3.38 -announcement production 3.38 -morality importance 3.31 -money operation 3.31 -delay news 3.31 -governor interview 3.25 -practice institution 3.19 -century nation 3.16 -coast forest 3.15 -shore woodland 3.08 -drink car 3.04 -president medal 3.0 -prejudice recognition 3.0 -viewer serial 2.97 -peace insurance 2.94 -Mars water 2.94 -media gain 2.88 -precedent cognition 2.81 -announcement effort 2.75 -line insurance 2.69 -crane implement 2.69 -drink mother 2.65 -opera industry 2.63 -volunteer motto 2.56 -listing proximity 2.56 -precedent collection 2.5 -cup article 2.4 -sign recess 2.38 -problem airport 2.38 -reason hypertension 2.31 -direction combination 2.25 -Wednesday news 2.22 -glass magician 2.08 -cemetery woodland 2.08 -possibility girl 1.94 -cup substance 1.92 -forest graveyard 1.85 -stock egg 1.81 -month hotel 1.81 -energy secretary 1.81 -precedent group 1.77 -production hike 1.75 -stock phone 1.62 -holy sex 1.62 -stock CD 1.31 -drink ear 1.31 -delay racism 1.19 -stock life 0.92 -stock jaguar 0.92 -monk slave 0.92 -lad wizard 0.92 -sugar approach 0.88 -rooster voyage 0.62 -noon string 0.54 -chord smile 0.54 -professor cucumber 0.31 -king cabbage 0.23 diff --git a/sematch/eval/word_similarity/noun_ws353.txt b/sematch/eval/word_similarity/noun_ws353.txt deleted file mode 100644 index 8ac0fc1..0000000 --- a/sematch/eval/word_similarity/noun_ws353.txt +++ /dev/null @@ -1,348 +0,0 @@ -love sex 6.77 -tiger cat 7.35 -tiger tiger 10.0 -book paper 7.46 -computer keyboard 7.62 -computer internet 7.58 -plane car 5.77 -train car 6.31 -telephone communication 7.5 -television radio 6.77 -media radio 7.42 -drug abuse 6.85 -bread butter 6.19 -cucumber potato 5.92 -doctor nurse 7.0 -professor doctor 6.62 -student professor 6.81 -smart student 4.62 -smart stupid 5.81 -company stock 7.08 -stock market 8.08 -stock phone 1.62 -stock CD 1.31 -stock jaguar 0.92 -stock egg 1.81 -fertility egg 6.69 -stock life 0.92 -book library 7.46 -bank money 8.12 -wood forest 7.73 -money cash 9.15 -professor cucumber 0.31 -king cabbage 0.23 -king queen 8.58 -king rook 5.92 -bishop rabbi 6.69 -Jerusalem Israel 8.46 -Jerusalem Palestinian 7.65 -holy sex 1.62 -fuck sex 9.44 -football soccer 9.03 -football basketball 6.81 -football tennis 6.63 -tennis racket 7.56 -Arafat peace 6.73 -Arafat terror 7.65 -Arafat Jackson 2.5 -law lawyer 8.38 -movie star 7.38 -movie popcorn 6.19 -movie critic 6.73 -movie theater 7.92 -physics proton 8.12 -physics chemistry 7.35 -space chemistry 4.88 -alcohol chemistry 5.54 -vodka gin 8.46 -vodka brandy 8.13 -drink car 3.04 -drink ear 1.31 -drink mouth 5.96 -baby mother 7.85 -drink mother 2.65 -car automobile 8.94 -gem jewel 8.96 -journey voyage 9.29 -boy lad 8.83 -coast shore 9.1 -asylum madhouse 8.87 -magician wizard 9.02 -midday noon 9.29 -furnace stove 8.79 -food fruit 7.52 -bird cock 7.1 -bird crane 7.38 -tool implement 6.46 -brother monk 6.27 -crane implement 2.69 -lad brother 4.46 -journey car 5.85 -monk oracle 5.0 -cemetery woodland 2.08 -food rooster 4.42 -coast hill 4.38 -forest graveyard 1.85 -shore woodland 3.08 -monk slave 0.92 -coast forest 3.15 -lad wizard 0.92 -chord smile 0.54 -glass magician 2.08 -noon string 0.54 -rooster voyage 0.62 -money dollar 8.42 -money cash 9.08 -money currency 9.04 -money wealth 8.27 -money property 7.57 -money possession 7.29 -money bank 8.5 -money deposit 7.73 -money withdrawal 6.88 -money laundering 5.65 -money operation 3.31 -tiger jaguar 8.0 -tiger feline 8.0 -tiger carnivore 7.08 -tiger mammal 6.85 -tiger animal 7.0 -tiger organism 4.77 -tiger fauna 5.62 -tiger zoo 5.87 -psychology psychiatry 8.08 -psychology anxiety 7.0 -psychology fear 6.85 -psychology depression 7.42 -psychology clinic 6.58 -psychology doctor 6.42 -psychology Freud 8.21 -psychology mind 7.69 -psychology health 7.23 -psychology science 6.71 -psychology discipline 5.58 -psychology cognition 7.48 -planet star 8.45 -planet constellation 8.06 -planet moon 8.08 -planet sun 8.02 -planet galaxy 8.11 -planet space 7.92 -planet astronomer 7.94 -precedent example 5.85 -precedent information 3.85 -precedent cognition 2.81 -precedent law 6.65 -precedent collection 2.5 -precedent group 1.77 -precedent antecedent 6.04 -cup coffee 6.58 -cup tableware 6.85 -cup article 2.4 -cup artifact 2.92 -cup object 3.69 -cup entity 2.15 -cup drink 7.25 -cup food 5.0 -cup substance 1.92 -cup liquid 5.9 -jaguar cat 7.42 -jaguar car 7.27 -energy secretary 1.81 -secretary senate 5.06 -energy laboratory 5.09 -computer laboratory 6.78 -weapon secret 6.06 -FBI fingerprint 6.94 -FBI investigation 8.31 -investigation effort 4.59 -Mars water 2.94 -Mars scientist 5.63 -news report 8.16 -canyon landscape 7.53 -image surface 4.56 -discovery space 6.34 -water seepage 6.56 -sign recess 2.38 -Wednesday news 2.22 -mile kilometer 8.66 -computer news 4.47 -territory surface 5.34 -atmosphere landscape 3.69 -president medal 3.0 -war troops 8.13 -record number 6.31 -skin eye 6.22 -Japanese American 6.5 -theater history 3.91 -volunteer motto 2.56 -prejudice recognition 3.0 -decoration valor 5.63 -century year 7.59 -century nation 3.16 -delay racism 1.19 -delay news 3.31 -minister party 6.63 -peace plan 4.75 -minority peace 3.69 -attempt peace 4.25 -government crisis 6.56 -deployment departure 4.25 -deployment withdrawal 5.88 -energy crisis 5.94 -announcement news 7.56 -announcement effort 2.75 -stroke hospital 7.03 -disability death 5.47 -victim emergency 6.47 -treatment recovery 7.91 -journal association 4.97 -doctor personnel 5.0 -doctor liability 5.19 -liability insurance 7.03 -school center 3.44 -reason hypertension 2.31 -reason criterion 5.91 -hundred percent 7.38 -Harvard Yale 8.13 -hospital infrastructure 4.63 -death row 5.25 -death inmate 5.03 -lawyer evidence 6.69 -life death 7.88 -life term 4.5 -word similarity 4.75 -board recommendation 4.47 -governor interview 3.25 -OPEC country 5.63 -peace atmosphere 3.69 -peace insurance 2.94 -territory kilometer 5.28 -travel activity 5.0 -competition price 6.44 -consumer confidence 4.13 -consumer energy 4.75 -problem airport 2.38 -car flight 4.94 -credit card 8.06 -credit information 5.31 -hotel reservation 8.03 -grocery money 5.94 -registration arrangement 6.0 -arrangement accommodation 5.41 -month hotel 1.81 -type kind 8.97 -arrival hotel 6.0 -bed closet 6.72 -closet clothes 8.0 -situation conclusion 4.81 -situation isolation 3.88 -impartiality interest 5.16 -direction combination 2.25 -street place 6.44 -street avenue 8.88 -street block 6.88 -street children 4.94 -listing proximity 2.56 -listing category 6.38 -cell phone 7.81 -production hike 1.75 -benchmark index 4.25 -media trading 3.88 -media gain 2.88 -dividend payment 7.63 -dividend calculation 6.48 -calculation computation 8.44 -currency market 7.5 -OPEC oil 8.59 -oil stock 6.34 -announcement production 3.38 -announcement warning 6.0 -profit warning 3.88 -profit loss 7.63 -dollar yen 7.78 -dollar buck 9.22 -dollar profit 7.38 -dollar loss 6.09 -computer software 8.5 -network hardware 8.31 -phone equipment 7.13 -equipment maker 5.91 -luxury car 6.47 -five month 3.38 -report gain 3.63 -liquid water 7.89 -baseball season 5.97 -game victory 7.03 -game team 7.69 -marathon sprint 7.47 -game series 6.19 -game defeat 6.97 -seven series 3.56 -seafood sea 7.47 -seafood food 8.34 -seafood lobster 8.7 -lobster food 7.81 -lobster wine 5.7 -food preparation 6.22 -video archive 6.34 -start year 4.06 -start match 4.47 -game round 5.97 -boxing round 7.61 -championship tournament 8.36 -line insurance 2.69 -day summer 3.94 -summer drought 7.16 -summer nature 5.63 -day dawn 7.53 -nature environment 8.31 -environment ecology 8.81 -nature man 6.25 -man woman 8.3 -man governor 5.25 -murder manslaughter 8.53 -soap opera 7.94 -opera performance 6.88 -life lesson 5.94 -focus life 4.06 -production crew 6.25 -television film 7.72 -lover quarrel 6.19 -viewer serial 2.97 -possibility girl 1.94 -population development 3.75 -morality importance 3.31 -morality marriage 3.69 -Mexico Brazil 7.44 -gender equality 6.41 -change attitude 5.44 -family planning 6.25 -opera industry 2.63 -sugar approach 0.88 -practice institution 3.19 -ministry culture 4.69 -problem challenge 6.75 -size prominence 5.31 -country citizen 7.31 -planet people 5.75 -development issue 3.97 -experience music 3.47 -music project 3.63 -glass metal 5.56 -aluminum metal 7.83 -chance credibility 3.88 -exhibit memorabilia 5.31 -concert virtuoso 6.81 -rock jazz 7.59 -museum theater 7.19 -observation architecture 4.38 -space world 6.53 -preservation world 6.19 -admission ticket 7.69 -shower thunderstorm 6.31 -shower flood 6.03 -weather forecast 8.34 -disaster area 6.25 -governor office 6.34 -architecture century 3.78 diff --git a/sematch/eval/word_similarity/results/graph_mc-jcn.txt b/sematch/eval/word_similarity/results/graph_mc-jcn.txt deleted file mode 100644 index 4e39bd6..0000000 --- a/sematch/eval/word_similarity/results/graph_mc-jcn.txt +++ /dev/null @@ -1,28 +0,0 @@ -0.794 -1.000 -1.000 -0.204 -0.226 -0.582 -0.238 -1.000 -0.060 -0.084 -0.182 -0.162 -0.604 -0.797 -0.068 -0.079 -0.059 -0.052 -0.059 -0.113 -0.052 -0.057 -0.068 -0.055 -0.068 -0.059 -0.056 -0.056 diff --git a/sematch/eval/word_similarity/results/graph_mc-lch.txt b/sematch/eval/word_similarity/results/graph_mc-lch.txt deleted file mode 100644 index 97a22a3..0000000 --- a/sematch/eval/word_similarity/results/graph_mc-lch.txt +++ /dev/null @@ -1,28 +0,0 @@ -0.699 -3.638 -3.638 -2.944 -2.944 -2.944 -2.944 -3.638 -1.335 -1.335 -2.944 -2.251 -2.944 -2.944 -2.028 -2.028 -1.558 -1.440 -0.865 -2.028 -1.440 -2.028 -2.028 -1.846 -2.028 -1.240 -1.558 -1.153 diff --git a/sematch/eval/word_similarity/results/graph_mc-li.txt b/sematch/eval/word_similarity/results/graph_mc-li.txt deleted file mode 100644 index 66e666d..0000000 --- a/sematch/eval/word_similarity/results/graph_mc-li.txt +++ /dev/null @@ -1,28 +0,0 @@ -0.696 -1.000 -1.000 -0.819 -0.819 -0.805 -0.819 -1.000 -0.163 -0.138 -0.819 -0.549 -0.818 -0.819 -0.449 -0.447 -0.246 -0.168 -0.027 -0.425 -0.168 -0.375 -0.449 -0.307 -0.449 -0.113 -0.206 -0.060 diff --git a/sematch/eval/word_similarity/results/graph_mc-lin.txt b/sematch/eval/word_similarity/results/graph_mc-lin.txt deleted file mode 100644 index 5296804..0000000 --- a/sematch/eval/word_similarity/results/graph_mc-lin.txt +++ /dev/null @@ -1,28 +0,0 @@ -0.736 -1.000 -1.000 -0.778 -0.831 -0.963 -0.856 -1.000 -0.228 -0.161 -0.774 -0.748 -0.947 -0.986 -0.255 -0.359 -0.226 -0.123 -0.092 -0.599 -0.123 -0.136 -0.254 -0.131 -0.255 -0.246 -0.214 -0.066 diff --git a/sematch/eval/word_similarity/results/graph_mc-path.txt b/sematch/eval/word_similarity/results/graph_mc-path.txt deleted file mode 100644 index aed3395..0000000 --- a/sematch/eval/word_similarity/results/graph_mc-path.txt +++ /dev/null @@ -1,28 +0,0 @@ -0.699 -1.000 -1.000 -0.500 -0.500 -0.500 -0.500 -1.000 -0.100 -0.100 -0.500 -0.250 -0.500 -0.500 -0.200 -0.200 -0.125 -0.111 -0.063 -0.200 -0.111 -0.200 -0.200 -0.167 -0.200 -0.091 -0.125 -0.083 diff --git a/sematch/eval/word_similarity/results/graph_mc-res.txt b/sematch/eval/word_similarity/results/graph_mc-res.txt deleted file mode 100644 index a05decf..0000000 --- a/sematch/eval/word_similarity/results/graph_mc-res.txt +++ /dev/null @@ -1,28 +0,0 @@ -0.713 -7.591 -12.068 -6.826 -8.399 -9.416 -9.475 -11.981 -2.306 -1.593 -7.678 -7.678 -5.877 -9.262 -2.334 -3.258 -2.334 -1.290 -0.802 -5.885 -1.290 -1.290 -2.334 -1.290 -2.334 -2.620 -2.283 -0.596 diff --git a/sematch/eval/word_similarity/results/graph_mc-res_graph.txt b/sematch/eval/word_similarity/results/graph_mc-res_graph.txt deleted file mode 100644 index 2a769f6..0000000 --- a/sematch/eval/word_similarity/results/graph_mc-res_graph.txt +++ /dev/null @@ -1,28 +0,0 @@ -0.717 -7.373 -12.329 -9.208 -10.796 -11.403 -10.070 -13.482 -2.462 -3.534 -9.321 -9.321 -10.199 -8.383 -1.543 -3.363 -1.543 -0.885 -0.855 -5.218 -0.885 -0.885 -1.543 -2.379 -1.543 -4.267 -1.534 -2.379 diff --git a/sematch/eval/word_similarity/results/graph_mc-wup.txt b/sematch/eval/word_similarity/results/graph_mc-wup.txt deleted file mode 100644 index 1798f09..0000000 --- a/sematch/eval/word_similarity/results/graph_mc-wup.txt +++ /dev/null @@ -1,28 +0,0 @@ -0.711 -1.000 -1.000 -0.952 -0.947 -0.909 -0.952 -0.667 -0.526 -0.400 -0.952 -0.870 -0.933 -0.952 -0.667 -0.750 -0.571 -0.429 -0.211 -0.667 -0.429 -0.600 -0.667 -0.545 -0.667 -0.375 -0.471 -0.267 diff --git a/sematch/eval/word_similarity/results/graph_rg-jcn.txt b/sematch/eval/word_similarity/results/graph_rg-jcn.txt deleted file mode 100644 index 1901867..0000000 --- a/sematch/eval/word_similarity/results/graph_rg-jcn.txt +++ /dev/null @@ -1,58 +0,0 @@ -0.762 -1.000 -1.000 -1.000 -0.650 -0.226 -1.000 -0.604 -1.000 -0.582 -0.409 -0.204 -0.176 -0.188 -0.291 -1.000 -1.000 -0.060 -0.238 -0.797 -0.084 -0.182 -0.162 -0.078 -0.053 -0.068 -0.079 -0.051 -0.063 -0.054 -0.060 -0.094 -0.075 -0.113 -0.064 -0.052 -0.059 -0.052 -0.068 -0.099 -0.078 -0.057 -0.059 -0.057 -0.055 -0.052 -0.068 -0.059 -0.061 -0.056 -0.045 -0.060 -0.068 -0.071 -0.067 -0.061 -0.056 -0.052 diff --git a/sematch/eval/word_similarity/results/graph_rg-lch.txt b/sematch/eval/word_similarity/results/graph_rg-lch.txt deleted file mode 100644 index 51b7f25..0000000 --- a/sematch/eval/word_similarity/results/graph_rg-lch.txt +++ /dev/null @@ -1,58 +0,0 @@ -0.782 -3.638 -3.638 -3.638 -2.944 -2.944 -3.638 -2.944 -3.638 -2.944 -2.944 -2.944 -2.251 -2.944 -2.944 -3.638 -3.638 -1.335 -2.944 -2.944 -1.335 -2.944 -2.251 -1.846 -1.846 -2.028 -2.028 -1.692 -1.692 -1.240 -1.846 -1.558 -1.558 -2.028 -1.440 -1.440 -0.865 -1.440 -2.028 -2.028 -1.440 -1.846 -1.558 -2.028 -1.846 -1.153 -2.028 -1.692 -1.153 -1.558 -0.930 -1.240 -1.692 -1.692 -1.073 -1.440 -1.153 -1.240 diff --git a/sematch/eval/word_similarity/results/graph_rg-li.txt b/sematch/eval/word_similarity/results/graph_rg-li.txt deleted file mode 100644 index 0a3dc85..0000000 --- a/sematch/eval/word_similarity/results/graph_rg-li.txt +++ /dev/null @@ -1,58 +0,0 @@ -0.779 -1.000 -1.000 -1.000 -0.818 -0.819 -1.000 -0.818 -0.995 -0.805 -0.818 -0.819 -0.549 -0.818 -0.818 -1.000 -1.000 -0.163 -0.819 -0.819 -0.138 -0.819 -0.549 -0.368 -0.367 -0.449 -0.447 -0.301 -0.251 -0.113 -0.307 -0.247 -0.243 -0.425 -0.138 -0.168 -0.027 -0.168 -0.449 -0.425 -0.201 -0.367 -0.246 -0.375 -0.307 -0.092 -0.449 -0.296 -0.110 -0.206 -0.051 -0.128 -0.296 -0.296 -0.086 -0.199 -0.060 -0.073 diff --git a/sematch/eval/word_similarity/results/graph_rg-lin.txt b/sematch/eval/word_similarity/results/graph_rg-lin.txt deleted file mode 100644 index 52a59f9..0000000 --- a/sematch/eval/word_similarity/results/graph_rg-lin.txt +++ /dev/null @@ -1,58 +0,0 @@ -0.776 -1.000 -1.000 -1.000 -0.977 -0.831 -1.000 -0.947 -1.000 -0.963 -0.941 -0.778 -0.795 -0.813 -0.874 -1.000 -1.000 -0.228 -0.856 -0.986 -0.161 -0.774 -0.748 -0.525 -0.205 -0.255 -0.359 -0.199 -0.237 -0.129 -0.141 -0.614 -0.271 -0.599 -0.149 -0.123 -0.092 -0.123 -0.255 -0.565 -0.356 -0.220 -0.226 -0.136 -0.131 -0.124 -0.254 -0.223 -0.223 -0.214 -0.108 -0.164 -0.237 -0.301 -0.181 -0.218 -0.066 -0.054 diff --git a/sematch/eval/word_similarity/results/graph_rg-path.txt b/sematch/eval/word_similarity/results/graph_rg-path.txt deleted file mode 100644 index 7c31880..0000000 --- a/sematch/eval/word_similarity/results/graph_rg-path.txt +++ /dev/null @@ -1,58 +0,0 @@ -0.782 -1.000 -1.000 -1.000 -0.500 -0.500 -1.000 -0.500 -1.000 -0.500 -0.500 -0.500 -0.250 -0.500 -0.500 -1.000 -1.000 -0.100 -0.500 -0.500 -0.100 -0.500 -0.250 -0.167 -0.167 -0.200 -0.200 -0.143 -0.143 -0.091 -0.167 -0.125 -0.125 -0.200 -0.111 -0.111 -0.063 -0.111 -0.200 -0.200 -0.111 -0.167 -0.125 -0.200 -0.167 -0.083 -0.200 -0.143 -0.083 -0.125 -0.067 -0.091 -0.143 -0.143 -0.077 -0.111 -0.083 -0.091 diff --git a/sematch/eval/word_similarity/results/graph_rg-res.txt b/sematch/eval/word_similarity/results/graph_rg-res.txt deleted file mode 100644 index 8dbd4ff..0000000 --- a/sematch/eval/word_similarity/results/graph_rg-res.txt +++ /dev/null @@ -1,58 +0,0 @@ -0.765 -12.068 -7.591 -11.288 -11.288 -8.399 -12.163 -5.877 -9.614 -9.416 -11.632 -6.826 -9.090 -9.442 -8.472 -9.966 -11.981 -2.306 -9.475 -9.262 -1.593 -7.678 -7.678 -6.513 -2.334 -2.334 -3.258 -2.334 -2.306 -1.290 -1.290 -7.678 -2.306 -5.885 -1.290 -1.290 -0.802 -1.290 -2.334 -5.885 -3.258 -2.334 -2.334 -1.290 -1.290 -1.290 -2.334 -2.306 -2.224 -2.283 -1.290 -1.532 -2.306 -3.258 -1.532 -2.306 -0.596 -0.596 diff --git a/sematch/eval/word_similarity/results/graph_rg-res_graph.txt b/sematch/eval/word_similarity/results/graph_rg-res_graph.txt deleted file mode 100644 index ec9851f..0000000 --- a/sematch/eval/word_similarity/results/graph_rg-res_graph.txt +++ /dev/null @@ -1,58 +0,0 @@ -0.723 -12.329 -7.373 -8.363 -15.274 -10.796 -9.321 -10.199 -12.971 -11.403 -5.320 -9.208 -9.250 -10.263 -6.262 -14.581 -13.482 -2.462 -10.070 -8.383 -3.534 -9.321 -9.321 -3.395 -1.543 -1.543 -3.363 -1.543 -2.462 -0.885 -0.885 -9.321 -2.462 -5.218 -2.379 -0.885 -0.855 -0.885 -1.543 -5.218 -3.363 -1.543 -1.543 -0.885 -2.379 -0.885 -1.543 -2.462 -1.482 -1.534 -0.885 -1.163 -2.462 -3.363 -1.163 -2.462 -2.379 -2.379 diff --git a/sematch/eval/word_similarity/results/graph_rg-wup.txt b/sematch/eval/word_similarity/results/graph_rg-wup.txt deleted file mode 100644 index 5921e0d..0000000 --- a/sematch/eval/word_similarity/results/graph_rg-wup.txt +++ /dev/null @@ -1,58 +0,0 @@ -0.738 -1.000 -1.000 -1.000 -0.933 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a/sematch/eval/word_similarity/results/graph_ws353-sim-path.txt b/sematch/eval/word_similarity/results/graph_ws353-sim-path.txt deleted file mode 100644 index 8fda5a5..0000000 --- a/sematch/eval/word_similarity/results/graph_ws353-sim-path.txt +++ /dev/null @@ -1,190 +0,0 @@ -0.602 -0.500 -1.000 -0.143 -0.167 -0.333 -0.250 -0.250 -0.250 -0.250 -0.143 -0.125 -1.000 -0.333 -1.000 -0.333 -0.200 -0.333 -0.500 -0.333 -0.167 -0.250 -0.125 -0.333 -0.333 -1.000 -1.000 -0.500 -0.500 -0.500 -0.500 -1.000 -0.100 -0.100 -0.500 -0.250 -0.063 -0.200 -0.500 -0.333 -0.333 -0.250 -0.167 -0.250 -0.333 -0.250 -0.143 -0.500 -0.333 -0.333 -0.250 -0.250 -0.500 -0.167 -0.333 -0.250 -0.167 -0.500 -0.059 -0.250 -0.167 -0.250 -0.333 -0.200 -0.083 -0.333 -0.083 -0.333 -0.167 -0.500 -0.167 -0.500 -0.167 -0.333 -0.333 -1.000 -0.167 -0.250 -1.000 -0.333 -0.500 -0.091 -0.500 -0.333 -0.250 -0.091 -0.333 -0.333 -0.200 -0.333 -0.100 -0.200 -0.167 -0.500 -0.333 -0.143 -0.143 -0.125 -0.167 -0.143 -0.100 -0.125 -0.167 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-0.706 -0.571 -0.588 -0.571 -0.700 -0.636 -0.500 -0.526 -0.308 diff --git a/sematch/eval/word_similarity/results/noun_mc-jcn.txt b/sematch/eval/word_similarity/results/noun_mc-jcn.txt deleted file mode 100644 index a173ecb..0000000 --- a/sematch/eval/word_similarity/results/noun_mc-jcn.txt +++ /dev/null @@ -1,31 +0,0 @@ -0.820 -1.000 -1.000 -0.204 -0.226 -0.582 -0.238 -1.000 -1.000 -0.060 -0.084 -0.182 -0.162 -0.604 -0.797 -0.068 -0.079 -0.065 -0.059 -0.052 -0.059 -0.113 -0.052 -0.057 -0.068 -0.055 -0.068 -0.059 -0.056 -0.042 -0.056 diff --git a/sematch/eval/word_similarity/results/noun_mc-lch.txt b/sematch/eval/word_similarity/results/noun_mc-lch.txt deleted file mode 100644 index f597af8..0000000 --- a/sematch/eval/word_similarity/results/noun_mc-lch.txt +++ /dev/null @@ -1,31 +0,0 @@ -0.724 -3.638 -3.638 -2.944 -2.944 -2.944 -2.944 -3.638 -3.638 -1.335 -1.335 -2.944 -2.251 -2.944 -2.944 -2.028 -2.028 -0.747 -1.558 -1.440 -0.865 -2.028 -1.440 -2.028 -2.028 -1.846 -2.028 -1.240 -1.558 -0.460 -1.153 diff --git a/sematch/eval/word_similarity/results/noun_mc-li.txt b/sematch/eval/word_similarity/results/noun_mc-li.txt deleted file mode 100644 index 4dad062..0000000 --- a/sematch/eval/word_similarity/results/noun_mc-li.txt +++ /dev/null @@ -1,31 +0,0 @@ -0.719 -1.000 -1.000 -0.819 -0.819 -0.805 -0.819 -1.000 -1.000 -0.163 -0.138 -0.819 -0.549 -0.818 -0.819 -0.449 -0.447 -0.000 -0.246 -0.168 -0.027 -0.425 -0.168 -0.375 -0.449 -0.307 -0.449 -0.113 -0.206 -0.000 -0.060 diff --git a/sematch/eval/word_similarity/results/noun_mc-lin.txt b/sematch/eval/word_similarity/results/noun_mc-lin.txt deleted file mode 100644 index 9220ae9..0000000 --- a/sematch/eval/word_similarity/results/noun_mc-lin.txt +++ /dev/null @@ -1,31 +0,0 @@ -0.752 -1.000 -1.000 -0.778 -0.831 -0.963 -0.856 -1.000 -1.000 -0.228 -0.161 -0.774 -0.748 -0.947 -0.986 -0.255 -0.359 -0.000 -0.226 -0.123 -0.092 -0.599 -0.123 -0.136 -0.254 -0.131 -0.255 -0.246 -0.214 -0.000 -0.066 diff --git a/sematch/eval/word_similarity/results/noun_mc-path.txt b/sematch/eval/word_similarity/results/noun_mc-path.txt deleted file mode 100644 index 1f95f05..0000000 --- a/sematch/eval/word_similarity/results/noun_mc-path.txt +++ /dev/null @@ -1,31 +0,0 @@ -0.724 -1.000 -1.000 -0.500 -0.500 -0.500 -0.500 -1.000 -1.000 -0.100 -0.100 -0.500 -0.250 -0.500 -0.500 -0.200 -0.200 -0.056 -0.125 -0.111 -0.063 -0.200 -0.111 -0.200 -0.200 -0.167 -0.200 -0.091 -0.125 -0.042 -0.083 diff --git a/sematch/eval/word_similarity/results/noun_mc-res.txt b/sematch/eval/word_similarity/results/noun_mc-res.txt deleted file mode 100644 index 353d19b..0000000 --- a/sematch/eval/word_similarity/results/noun_mc-res.txt +++ /dev/null @@ -1,31 +0,0 @@ -0.733 -7.591 -12.068 -6.826 -8.399 -9.416 -9.475 -11.981 -11.064 -2.306 -1.593 -7.678 -7.678 -5.877 -9.262 -2.334 -3.258 -0.000 -2.334 -1.290 -0.802 -5.885 -1.290 -1.290 -2.334 -1.290 -2.334 -2.620 -2.283 -0.000 -0.596 diff --git a/sematch/eval/word_similarity/results/noun_mc-wpath_mc.txt b/sematch/eval/word_similarity/results/noun_mc-wpath_mc.txt deleted file mode 100644 index 05921da..0000000 --- a/sematch/eval/word_similarity/results/noun_mc-wpath_mc.txt +++ /dev/null @@ -1,31 +0,0 @@ -0.740 -1.000 -1.000 -0.998 -1.000 -1.000 -1.000 -1.000 -1.000 -0.479 -0.323 -0.999 -0.997 -0.995 -1.000 -0.680 -0.832 -0.056 -0.548 -0.290 -0.122 -0.982 -0.290 -0.449 -0.680 -0.395 -0.680 -0.524 -0.536 -0.042 -0.136 diff --git a/sematch/eval/word_similarity/results/noun_mc-wpath_rg.txt b/sematch/eval/word_similarity/results/noun_mc-wpath_rg.txt deleted file mode 100644 index ab12dcd..0000000 --- a/sematch/eval/word_similarity/results/noun_mc-wpath_rg.txt +++ /dev/null @@ -1,31 +0,0 @@ -0.726 -1.000 -1.000 -0.672 -0.708 -0.729 -0.731 -1.000 -1.000 -0.124 -0.116 -0.692 -0.428 -0.650 -0.726 -0.242 -0.261 -0.056 -0.154 -0.125 -0.068 -0.317 -0.125 -0.223 -0.242 -0.186 -0.242 -0.116 -0.154 -0.042 -0.088 diff --git a/sematch/eval/word_similarity/results/noun_mc-wpath_simlex.txt b/sematch/eval/word_similarity/results/noun_mc-wpath_simlex.txt deleted file mode 100644 index 07705e3..0000000 --- a/sematch/eval/word_similarity/results/noun_mc-wpath_simlex.txt +++ /dev/null @@ -1,31 +0,0 @@ -0.728 -1.000 -1.000 -0.821 -0.867 -0.891 -0.892 -1.000 -1.000 -0.157 -0.137 -0.847 -0.649 -0.788 -0.888 -0.296 -0.341 -0.056 -0.194 -0.143 -0.074 -0.482 -0.143 -0.250 -0.296 -0.211 -0.296 -0.152 -0.192 -0.042 -0.094 diff --git a/sematch/eval/word_similarity/results/noun_mc-wpath_ws353.txt b/sematch/eval/word_similarity/results/noun_mc-wpath_ws353.txt deleted file mode 100644 index 4020459..0000000 --- a/sematch/eval/word_similarity/results/noun_mc-wpath_ws353.txt +++ /dev/null @@ -1,31 +0,0 @@ -0.738 -1.000 -1.000 -0.991 -0.997 -0.999 -0.999 -1.000 -1.000 -0.355 -0.251 -0.995 -0.986 -0.983 -0.998 -0.558 -0.705 -0.056 -0.419 -0.234 -0.104 -0.937 -0.234 -0.379 -0.558 -0.328 -0.558 -0.381 -0.410 -0.042 -0.121 diff --git a/sematch/eval/word_similarity/results/noun_mc-wpath_ws353sim.txt b/sematch/eval/word_similarity/results/noun_mc-wpath_ws353sim.txt deleted file mode 100644 index 07705e3..0000000 --- a/sematch/eval/word_similarity/results/noun_mc-wpath_ws353sim.txt +++ /dev/null @@ -1,31 +0,0 @@ -0.728 -1.000 -1.000 -0.821 -0.867 -0.891 -0.892 -1.000 -1.000 -0.157 -0.137 -0.847 -0.649 -0.788 -0.888 -0.296 -0.341 -0.056 -0.194 -0.143 -0.074 -0.482 -0.143 -0.250 -0.296 -0.211 -0.296 -0.152 -0.192 -0.042 -0.094 diff --git a/sematch/eval/word_similarity/results/noun_mc-wup.txt b/sematch/eval/word_similarity/results/noun_mc-wup.txt deleted file mode 100644 index 9698619..0000000 --- a/sematch/eval/word_similarity/results/noun_mc-wup.txt +++ /dev/null @@ -1,31 +0,0 @@ -0.729 -1.000 -1.000 -0.952 -0.947 -0.909 -0.952 -0.667 -1.000 -0.526 -0.400 -0.952 -0.870 -0.933 -0.952 -0.667 -0.750 -0.105 -0.571 -0.429 -0.211 -0.667 -0.429 -0.600 -0.667 -0.545 -0.667 -0.375 -0.471 -0.080 -0.267 diff --git a/sematch/eval/word_similarity/results/noun_rg-jcn.txt b/sematch/eval/word_similarity/results/noun_rg-jcn.txt deleted file mode 100644 index f809383..0000000 --- a/sematch/eval/word_similarity/results/noun_rg-jcn.txt +++ /dev/null @@ -1,66 +0,0 @@ -0.775 -1.000 -1.000 -1.000 -1.000 -0.650 -0.226 -1.000 -0.604 -1.000 -0.582 -0.409 -0.204 -0.176 -1.000 -0.188 -0.291 -1.000 -1.000 -0.060 -0.238 -0.797 -0.084 -0.182 -0.162 -0.078 -0.053 -0.068 -0.079 -0.051 -0.063 -0.054 -0.065 -0.060 -0.094 -0.075 -0.113 -0.064 -0.047 -0.052 -0.059 -0.052 -0.068 -0.099 -0.078 -0.057 -0.059 -0.057 -0.051 -0.055 -0.052 -0.068 -0.059 -0.061 -0.056 -0.045 -0.060 -0.068 -0.061 -0.071 -0.067 -0.048 -0.061 -0.056 -0.042 -0.052 diff --git a/sematch/eval/word_similarity/results/noun_rg-lch.txt b/sematch/eval/word_similarity/results/noun_rg-lch.txt deleted file mode 100644 index 3af09c0..0000000 --- a/sematch/eval/word_similarity/results/noun_rg-lch.txt +++ /dev/null @@ -1,66 +0,0 @@ -0.781 -3.638 -3.638 -3.638 -3.638 -2.944 -2.944 -3.638 -2.944 -3.638 -2.944 -2.944 -2.944 -2.251 -3.638 -2.944 -2.944 -3.638 -3.638 -1.335 -2.944 -2.944 -1.335 -2.944 -2.251 -1.846 -1.846 -2.028 -2.028 -1.692 -1.692 -1.240 -0.747 -1.846 -1.558 -1.558 -2.028 -1.440 -0.930 -1.440 -0.865 -1.440 -2.028 -2.028 -1.440 -1.846 -1.558 -2.028 -1.153 -1.846 -1.153 -2.028 -1.692 -1.153 -1.558 -0.930 -1.240 -1.692 -1.073 -1.692 -1.073 -1.335 -1.440 -1.153 -0.460 -1.240 diff --git a/sematch/eval/word_similarity/results/noun_rg-li.txt b/sematch/eval/word_similarity/results/noun_rg-li.txt deleted file mode 100644 index 8166646..0000000 --- a/sematch/eval/word_similarity/results/noun_rg-li.txt +++ /dev/null @@ -1,66 +0,0 @@ -0.787 -1.000 -1.000 -1.000 -1.000 -0.818 -0.819 -1.000 -0.818 -0.995 -0.805 -0.818 -0.819 -0.549 -0.999 -0.818 -0.818 -1.000 -1.000 -0.163 -0.819 -0.819 -0.138 -0.819 -0.549 -0.368 -0.367 -0.449 -0.447 -0.301 -0.251 -0.113 -0.000 -0.307 -0.247 -0.243 -0.425 -0.138 -0.000 -0.168 -0.027 -0.168 -0.449 -0.425 -0.201 -0.367 -0.246 -0.375 -0.000 -0.307 -0.092 -0.449 -0.296 -0.110 -0.206 -0.051 -0.128 -0.296 -0.000 -0.296 -0.086 -0.000 -0.199 -0.060 -0.000 -0.073 diff --git a/sematch/eval/word_similarity/results/noun_rg-lin.txt b/sematch/eval/word_similarity/results/noun_rg-lin.txt deleted file mode 100644 index 5c313b7..0000000 --- a/sematch/eval/word_similarity/results/noun_rg-lin.txt +++ /dev/null @@ -1,66 +0,0 @@ -0.784 -1.000 -1.000 -1.000 -1.000 -0.977 -0.831 -1.000 -0.947 -1.000 -0.963 -0.941 -0.778 -0.795 -1.000 -0.813 -0.874 -1.000 -1.000 -0.228 -0.856 -0.986 -0.161 -0.774 -0.748 -0.525 -0.205 -0.255 -0.359 -0.199 -0.237 -0.129 -0.000 -0.141 -0.614 -0.271 -0.599 -0.149 -0.000 -0.123 -0.092 -0.123 -0.255 -0.565 -0.356 -0.220 -0.226 -0.136 -0.000 -0.131 -0.124 -0.254 -0.223 -0.223 -0.214 -0.108 -0.164 -0.237 -0.000 -0.301 -0.181 -0.000 -0.218 -0.066 -0.000 -0.054 diff --git a/sematch/eval/word_similarity/results/noun_rg-path.txt b/sematch/eval/word_similarity/results/noun_rg-path.txt deleted file mode 100644 index 4b11c4c..0000000 --- a/sematch/eval/word_similarity/results/noun_rg-path.txt +++ /dev/null @@ -1,66 +0,0 @@ -0.781 -1.000 -1.000 -1.000 -1.000 -0.500 -0.500 -1.000 -0.500 -1.000 -0.500 -0.500 -0.500 -0.250 -1.000 -0.500 -0.500 -1.000 -1.000 -0.100 -0.500 -0.500 -0.100 -0.500 -0.250 -0.167 -0.167 -0.200 -0.200 -0.143 -0.143 -0.091 -0.056 -0.167 -0.125 -0.125 -0.200 -0.111 -0.067 -0.111 -0.063 -0.111 -0.200 -0.200 -0.111 -0.167 -0.125 -0.200 -0.083 -0.167 -0.083 -0.200 -0.143 -0.083 -0.125 -0.067 -0.091 -0.143 -0.077 -0.143 -0.077 -0.100 -0.111 -0.083 -0.042 -0.091 diff --git a/sematch/eval/word_similarity/results/noun_rg-res.txt b/sematch/eval/word_similarity/results/noun_rg-res.txt deleted file mode 100644 index befdb5b..0000000 --- a/sematch/eval/word_similarity/results/noun_rg-res.txt +++ /dev/null @@ -1,66 +0,0 @@ -0.776 -12.068 -11.064 -7.591 -11.288 -11.288 -8.399 -12.163 -5.877 -9.614 -9.416 -11.632 -6.826 -9.090 -9.583 -9.442 -8.472 -9.966 -11.981 -2.306 -9.475 -9.262 -1.593 -7.678 -7.678 -6.513 -2.334 -2.334 -3.258 -2.334 -2.306 -1.290 -0.000 -1.290 -7.678 -2.306 -5.885 -1.290 -0.000 -1.290 -0.802 -1.290 -2.334 -5.885 -3.258 -2.334 -2.334 -1.290 -0.000 -1.290 -1.290 -2.334 -2.306 -2.224 -2.283 -1.290 -1.532 -2.306 -0.000 -3.258 -1.532 -0.000 -2.306 -0.596 -0.000 -0.596 diff --git a/sematch/eval/word_similarity/results/noun_rg-wpath_mc.txt b/sematch/eval/word_similarity/results/noun_rg-wpath_mc.txt deleted file mode 100644 index 1697bd4..0000000 --- a/sematch/eval/word_similarity/results/noun_rg-wpath_mc.txt +++ /dev/null @@ -1,66 +0,0 @@ -0.785 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.995 -1.000 -1.000 -1.000 -0.998 -0.999 -1.000 -1.000 -1.000 -1.000 -1.000 -0.479 -1.000 -1.000 -0.323 -0.999 -0.997 -0.987 -0.629 -0.680 -0.832 -0.586 -0.574 -0.246 -0.056 -0.395 -0.994 -0.542 -0.982 -0.266 -0.067 -0.290 -0.122 -0.290 -0.680 -0.982 -0.712 -0.629 -0.548 -0.449 -0.083 -0.395 -0.229 -0.680 -0.580 -0.411 -0.536 -0.189 -0.289 -0.580 -0.077 -0.739 -0.253 -0.100 -0.508 -0.136 -0.042 -0.147 diff --git a/sematch/eval/word_similarity/results/noun_rg-wpath_rg.txt b/sematch/eval/word_similarity/results/noun_rg-wpath_rg.txt deleted file mode 100644 index 602b4b8..0000000 --- a/sematch/eval/word_similarity/results/noun_rg-wpath_rg.txt +++ /dev/null @@ -1,66 +0,0 @@ -0.795 -1.000 -1.000 -1.000 -1.000 -0.767 -0.708 -1.000 -0.650 -1.000 -0.729 -0.773 -0.672 -0.465 -1.000 -0.730 -0.709 -1.000 -1.000 -0.124 -0.731 -0.726 -0.116 -0.692 -0.428 -0.284 -0.204 -0.242 -0.261 -0.176 -0.175 -0.103 -0.056 -0.186 -0.243 -0.154 -0.317 -0.120 -0.067 -0.125 -0.068 -0.125 -0.242 -0.317 -0.150 -0.204 -0.154 -0.223 -0.083 -0.186 -0.094 -0.242 -0.175 -0.103 -0.154 -0.076 -0.105 -0.175 -0.077 -0.175 -0.089 -0.100 -0.137 -0.088 -0.042 -0.096 diff --git a/sematch/eval/word_similarity/results/noun_rg-wpath_simlex.txt b/sematch/eval/word_similarity/results/noun_rg-wpath_simlex.txt deleted file mode 100644 index 10cf290..0000000 --- a/sematch/eval/word_similarity/results/noun_rg-wpath_simlex.txt +++ /dev/null @@ -1,66 +0,0 @@ -0.794 -1.000 -1.000 -1.000 -1.000 -0.925 -0.867 -1.000 -0.788 -1.000 -0.891 -0.931 -0.821 -0.717 -1.000 -0.892 -0.869 -1.000 -1.000 -0.157 -0.892 -0.888 -0.137 -0.847 -0.649 -0.461 -0.252 -0.296 -0.341 -0.219 -0.217 -0.118 -0.056 -0.211 -0.442 -0.193 -0.482 -0.130 -0.067 -0.143 -0.074 -0.143 -0.296 -0.482 -0.205 -0.252 -0.194 -0.250 -0.083 -0.211 -0.108 -0.296 -0.218 -0.130 -0.192 -0.087 -0.123 -0.218 -0.077 -0.228 -0.105 -0.100 -0.173 -0.094 -0.042 -0.103 diff --git a/sematch/eval/word_similarity/results/noun_rg-wpath_ws353.txt b/sematch/eval/word_similarity/results/noun_rg-wpath_ws353.txt deleted file mode 100644 index f29fc66..0000000 --- a/sematch/eval/word_similarity/results/noun_rg-wpath_ws353.txt +++ /dev/null @@ -1,66 +0,0 @@ -0.790 -1.000 -1.000 -1.000 -1.000 -1.000 -0.997 -1.000 -0.983 -1.000 -0.999 -1.000 -0.991 -0.995 -1.000 -0.999 -0.997 -1.000 -1.000 -0.355 -0.999 -0.998 -0.251 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a/sematch/eval/word_similarity/results/noun_ws353-sim-li.txt b/sematch/eval/word_similarity/results/noun_ws353-sim-li.txt deleted file mode 100644 index 163354c..0000000 --- a/sematch/eval/word_similarity/results/noun_ws353-sim-li.txt +++ /dev/null @@ -1,202 +0,0 @@ -0.628 -0.819 -1.000 -0.301 -0.367 -0.670 -0.548 -0.546 -0.548 -0.549 -0.301 -0.246 -0.000 -0.995 -0.669 -1.000 -0.670 -0.449 -0.667 -0.819 -0.670 -0.368 -0.549 -0.206 -0.670 -0.670 -1.000 -1.000 -0.819 -0.819 -0.805 -0.819 -1.000 -1.000 -0.163 -0.138 -0.819 -0.549 -0.027 -0.449 -0.818 -0.670 -0.670 -0.549 -0.368 -0.546 -0.667 -0.546 -0.301 -0.818 -0.669 -0.667 -0.546 -0.546 -0.815 -0.348 -0.670 -0.540 -0.307 -0.000 -0.819 -0.039 -0.546 -0.362 -0.549 -0.659 -0.425 -0.060 -0.670 -0.060 -0.667 -0.362 -0.819 -0.348 -0.819 -0.362 -0.670 -0.670 -1.000 -0.366 -0.546 -1.000 -0.669 -0.818 -0.133 -0.805 -0.667 -0.540 -0.113 -0.667 -0.670 -0.449 -0.670 -0.089 -0.449 -0.307 -0.818 -0.669 -0.296 -0.301 -0.246 -0.362 -0.162 -0.157 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a/sematch/eval/word_similarity/results/noun_ws353-sim-wpath_ws353.txt b/sematch/eval/word_similarity/results/noun_ws353-sim-wpath_ws353.txt deleted file mode 100644 index 7d2526c..0000000 --- a/sematch/eval/word_similarity/results/noun_ws353-sim-wpath_ws353.txt +++ /dev/null @@ -1,202 +0,0 @@ -0.644 -0.999 -1.000 -0.910 -0.916 -0.996 -0.977 -0.975 -0.994 -0.988 -0.933 -0.419 -0.071 -1.000 -0.978 -1.000 -0.995 -0.969 -0.981 -1.000 -0.995 -0.988 -0.973 -0.591 -0.996 -0.996 -1.000 -1.000 -0.991 -0.997 -0.999 -0.999 -1.000 -1.000 -0.355 -0.251 -0.995 -0.986 -0.104 -0.956 -0.989 -0.998 -0.995 -0.988 -0.895 -0.794 -0.700 -0.794 -0.960 -0.993 -0.960 -0.984 -0.976 -0.976 -0.996 -0.742 -0.989 -0.622 -0.381 -0.167 -0.999 -0.153 -0.957 -0.751 -0.958 -0.874 -0.783 -0.121 -0.998 -0.121 -0.988 -0.502 -0.988 -0.638 -0.998 -0.508 -0.995 -0.982 -1.000 -0.935 -0.975 -1.000 -0.951 -0.996 -0.335 -0.986 -0.997 -0.958 -0.503 -0.980 -0.938 -0.577 -0.995 -0.162 -0.946 -0.669 -0.978 -0.994 -0.452 -0.940 -0.419 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-0.052 -0.057 -0.068 -0.055 -0.056 diff --git a/sematch/eval/word_similarity/results/type_mc-jcn_graph.txt b/sematch/eval/word_similarity/results/type_mc-jcn_graph.txt deleted file mode 100644 index 4a5428d..0000000 --- a/sematch/eval/word_similarity/results/type_mc-jcn_graph.txt +++ /dev/null @@ -1,19 +0,0 @@ --0.324 -15.355 -24.946 -6.059 -1.158 -22.779 -17.156 -26.659 -1.200 -17.640 -22.114 -16.535 -20.565 -25.372 -20.565 -23.605 -15.548 -23.762 -22.420 diff --git a/sematch/eval/word_similarity/results/type_mc-lch.txt b/sematch/eval/word_similarity/results/type_mc-lch.txt deleted file mode 100644 index c20de3e..0000000 --- a/sematch/eval/word_similarity/results/type_mc-lch.txt +++ /dev/null @@ -1,19 +0,0 @@ -0.621 -3.638 -3.638 -2.944 -2.944 -3.638 -1.335 -2.944 -2.944 -2.944 -0.747 -1.558 -1.440 -2.028 -1.440 -2.028 -2.028 -1.846 -1.558 diff --git a/sematch/eval/word_similarity/results/type_mc-li.txt b/sematch/eval/word_similarity/results/type_mc-li.txt deleted file mode 100644 index 032b603..0000000 --- a/sematch/eval/word_similarity/results/type_mc-li.txt +++ /dev/null @@ -1,19 +0,0 @@ -0.614 -1.000 -1.000 -0.819 -0.805 -1.000 -0.138 -0.819 -0.818 -0.819 -0.000 -0.246 -0.168 -0.425 -0.168 -0.375 -0.449 -0.307 -0.206 diff --git a/sematch/eval/word_similarity/results/type_mc-lin.txt b/sematch/eval/word_similarity/results/type_mc-lin.txt deleted file mode 100644 index 65145c8..0000000 --- a/sematch/eval/word_similarity/results/type_mc-lin.txt +++ /dev/null @@ -1,19 +0,0 @@ -0.696 -1.000 -1.000 -0.778 -0.963 -1.000 -0.161 -0.774 -0.947 -0.986 -0.000 -0.226 -0.123 -0.599 -0.123 -0.136 -0.254 -0.131 -0.214 diff --git a/sematch/eval/word_similarity/results/type_mc-lin_graph.txt b/sematch/eval/word_similarity/results/type_mc-lin_graph.txt deleted file mode 100644 index dd18451..0000000 --- a/sematch/eval/word_similarity/results/type_mc-lin_graph.txt +++ /dev/null @@ -1,19 +0,0 @@ -0.661 -1.000 -1.000 -0.830 -0.993 -1.000 -0.100 -0.102 -0.990 -0.167 -0.000 -0.166 -0.083 -0.523 -0.083 -0.073 -0.175 -0.072 -0.120 diff --git a/sematch/eval/word_similarity/results/type_mc-path.txt b/sematch/eval/word_similarity/results/type_mc-path.txt deleted file mode 100644 index 1c9f9d4..0000000 --- a/sematch/eval/word_similarity/results/type_mc-path.txt +++ /dev/null @@ -1,19 +0,0 @@ -0.621 -1.000 -1.000 -0.500 -0.500 -1.000 -0.100 -0.500 -0.500 -0.500 -0.056 -0.125 -0.111 -0.200 -0.111 -0.200 -0.200 -0.167 -0.125 diff --git a/sematch/eval/word_similarity/results/type_mc-res.txt b/sematch/eval/word_similarity/results/type_mc-res.txt deleted file mode 100644 index abe241e..0000000 --- a/sematch/eval/word_similarity/results/type_mc-res.txt +++ /dev/null @@ -1,19 +0,0 @@ -0.679 -7.591 -12.068 -6.826 -9.416 -11.981 -1.593 -7.678 -5.877 -9.262 -0.000 -2.334 -1.290 -5.885 -1.290 -1.290 -2.334 -1.290 -2.283 diff --git a/sematch/eval/word_similarity/results/type_mc-res_graph.txt b/sematch/eval/word_similarity/results/type_mc-res_graph.txt deleted file mode 100644 index b81b560..0000000 --- a/sematch/eval/word_similarity/results/type_mc-res_graph.txt +++ /dev/null @@ -1,19 +0,0 @@ -0.704 -7.373 -12.329 -9.208 -11.403 -13.482 -3.534 -9.321 -10.199 -8.383 -0.000 -1.543 -0.885 -5.218 -0.885 -0.885 -1.543 -2.379 -1.534 diff --git a/sematch/eval/word_similarity/results/type_mc-wup.txt b/sematch/eval/word_similarity/results/type_mc-wup.txt deleted file mode 100644 index d744557..0000000 --- a/sematch/eval/word_similarity/results/type_mc-wup.txt +++ /dev/null @@ -1,19 +0,0 @@ -0.606 -1.000 -1.000 -0.952 -0.909 -0.667 -0.400 -0.952 -0.933 -0.952 -0.105 -0.571 -0.429 -0.667 -0.429 -0.600 -0.667 -0.545 -0.471 diff --git a/sematch/eval/word_similarity/results/type_rg-jcn.txt b/sematch/eval/word_similarity/results/type_rg-jcn.txt deleted file mode 100644 index 97708d2..0000000 --- a/sematch/eval/word_similarity/results/type_rg-jcn.txt +++ /dev/null @@ -1,42 +0,0 @@ -0.676 -1.000 -1.000 -1.000 -0.650 -0.604 -1.000 -0.582 -0.204 -0.176 -1.000 -1.000 -0.797 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a/sematch/eval/word_similarity/results/type_rg-lch.txt +++ /dev/null @@ -1,42 +0,0 @@ -0.679 -3.638 -3.638 -3.638 -2.944 -2.944 -3.638 -2.944 -2.944 -2.251 -3.638 -3.638 -2.944 -1.335 -2.944 -1.846 -1.846 -1.692 -1.692 -1.240 -0.747 -1.846 -1.558 -2.028 -1.440 -0.930 -1.440 -1.440 -2.028 -1.440 -1.846 -1.558 -2.028 -1.846 -1.153 -2.028 -1.692 -1.558 -1.240 -1.692 -1.073 -1.440 diff --git a/sematch/eval/word_similarity/results/type_rg-li.txt b/sematch/eval/word_similarity/results/type_rg-li.txt deleted file mode 100644 index 5007357..0000000 --- a/sematch/eval/word_similarity/results/type_rg-li.txt +++ /dev/null @@ -1,42 +0,0 @@ -0.673 -1.000 -1.000 -1.000 -0.818 -0.818 -0.995 -0.805 -0.819 -0.549 -1.000 -1.000 -0.819 -0.138 -0.819 -0.368 -0.367 -0.301 -0.251 -0.113 -0.000 -0.307 -0.243 -0.425 -0.138 -0.000 -0.168 -0.168 -0.425 -0.201 -0.367 -0.246 -0.375 -0.307 -0.092 -0.449 -0.296 -0.206 -0.128 -0.296 -0.086 -0.199 diff --git a/sematch/eval/word_similarity/results/type_rg-lin.txt 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a/sematch/eval/word_similarity/results/type_ws353-path.txt b/sematch/eval/word_similarity/results/type_ws353-path.txt deleted file mode 100644 index 2ccc0d2..0000000 --- a/sematch/eval/word_similarity/results/type_ws353-path.txt +++ /dev/null @@ -1,212 +0,0 @@ -0.353 -0.500 -0.500 -1.000 -0.333 -0.250 -0.125 -0.143 -0.167 -0.091 -0.100 -0.250 -0.143 -0.125 -0.167 -0.143 -0.143 -0.167 -0.167 -0.143 -0.167 -0.333 -0.143 -1.000 -1.000 -0.200 -0.100 -0.091 -0.125 -0.091 -0.143 -0.333 -0.333 -0.091 -0.200 -0.167 -1.000 -1.000 -0.500 -0.500 -1.000 -0.100 -0.500 -0.500 -0.500 -0.056 -0.125 -0.111 -0.200 -0.111 -0.200 -0.200 -0.167 -0.125 -0.500 -0.500 -0.333 -0.250 -0.143 -0.333 -0.091 -0.111 -0.333 -0.333 -0.250 -0.167 -0.250 -0.333 -0.250 -0.125 -0.333 -0.250 -0.125 -0.143 -0.167 -0.167 -0.333 -0.200 -0.250 -0.167 -0.200 -0.167 -0.143 -0.167 -0.500 -0.059 -0.091 -0.100 -0.111 -0.091 -0.250 -0.500 -0.083 -0.200 -0.143 -0.200 -0.091 -0.167 -0.111 -0.077 -0.100 -0.500 -0.143 -0.083 -0.167 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diff --git a/sematch/eval/word_similarity/results/type_ws353-sim-li.txt b/sematch/eval/word_similarity/results/type_ws353-sim-li.txt deleted file mode 100644 index b01b5e4..0000000 --- a/sematch/eval/word_similarity/results/type_ws353-sim-li.txt +++ /dev/null @@ -1,129 +0,0 @@ -0.612 -0.819 -1.000 -0.301 -0.367 -0.549 -0.301 -0.246 -0.995 -1.000 -0.449 -0.670 -0.670 -1.000 -1.000 -0.819 -0.805 -1.000 -0.138 -0.819 -0.818 -0.670 -0.670 -0.549 -0.368 -0.546 -0.667 -0.546 -0.667 -0.670 -0.540 -0.307 -0.819 -0.039 -0.425 -0.060 -0.060 -0.667 -0.362 -0.819 -0.348 -0.819 -0.362 -0.670 -0.670 -1.000 -0.669 -0.818 -0.805 -0.667 -0.540 -0.113 -0.667 -0.670 -0.449 -0.089 -0.307 -0.296 -0.246 -0.362 -0.162 -0.157 -0.243 -0.296 -0.108 -0.089 -0.089 -0.233 -0.548 -0.442 -0.667 -0.285 -0.000 -0.060 -0.366 -0.425 -0.442 -0.540 -0.089 -0.307 -0.458 -0.251 -0.073 -0.442 -0.307 -0.348 -0.191 -0.132 -0.546 -0.113 -0.348 -0.669 -0.206 -0.251 -0.108 -0.233 -0.659 -0.307 -0.375 -0.073 -0.040 -0.362 -0.133 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diff --git a/sematch/eval/word_similarity/results/type_ws353-sim-res_graph.txt b/sematch/eval/word_similarity/results/type_ws353-sim-res_graph.txt deleted file mode 100644 index 64449d6..0000000 --- a/sematch/eval/word_similarity/results/type_ws353-sim-res_graph.txt +++ /dev/null @@ -1,129 +0,0 @@ -0.490 -12.096 -10.808 -5.893 -5.869 -5.860 -4.286 -1.543 -10.368 -14.581 -5.405 -10.774 -10.774 -7.373 -12.329 -9.208 -11.403 -13.482 -3.534 -9.321 -8.967 -12.096 -10.774 -9.407 -7.056 -4.848 -1.482 -4.848 -8.530 -8.668 -2.462 -4.267 -12.096 -1.163 -6.270 -2.379 -2.379 -6.637 -4.730 -6.439 -6.301 -7.866 -5.833 -10.512 -13.328 -10.956 -8.595 -13.887 -8.103 -10.709 -8.103 -5.582 -5.585 -4.040 -3.363 -2.379 -5.582 -2.462 -1.543 -7.283 -2.379 -1.163 -2.462 -4.730 -5.668 -2.379 -0.855 -4.730 -8.285 -5.668 -14.175 -6.637 -0.000 -2.379 -5.253 -5.218 -4.730 -8.057 -2.379 -6.637 -9.396 -3.534 -2.379 -6.749 -5.833 -6.301 -2.379 -2.379 -5.253 -5.245 -5.181 -5.181 -6.637 -4.267 -2.379 -3.729 -12.876 -2.379 -0.885 -0.855 -2.379 -10.354 -2.462 -12.635 -5.833 -2.379 -10.819 -6.792 -2.379 -0.000 -8.668 -4.112 -0.000 -5.253 -1.534 -0.885 -0.000 -6.792 -0.885 -7.283 -0.000 -0.855 -6.262 -0.855 -6.262 -6.301 -7.677 -1.543 -2.379 diff --git a/sematch/eval/word_similarity/results/type_ws353-sim-wup.txt b/sematch/eval/word_similarity/results/type_ws353-sim-wup.txt deleted file mode 100644 index 31c6be3..0000000 --- a/sematch/eval/word_similarity/results/type_ws353-sim-wup.txt +++ /dev/null @@ -1,129 +0,0 @@ -0.589 -0.966 -1.000 -0.727 -0.737 -0.870 -0.545 -0.571 -1.000 -1.000 -0.545 -0.909 -0.909 -1.000 -1.000 -0.952 -0.909 -0.667 -0.400 -0.952 -0.933 -0.933 -0.929 -0.889 -0.800 -0.800 -0.857 -0.800 -0.857 -0.889 -0.769 -0.545 -0.966 -0.333 -0.667 -0.267 -0.267 -0.857 -0.667 -0.947 -0.615 -0.947 -0.667 -0.900 -0.889 -1.000 -0.875 -0.933 -0.909 -0.857 -0.769 -0.375 -0.857 -0.706 -0.667 -0.308 -0.545 -0.625 -0.571 -0.667 -0.400 -0.471 -0.588 -0.625 -0.455 -0.308 -0.308 -0.533 -0.824 -0.714 -0.857 -0.571 -0.167 -0.267 -0.706 -0.667 -0.714 -0.769 -0.308 -0.545 -0.750 -0.500 -0.286 -0.714 -0.545 -0.615 -0.500 -0.364 -0.800 -0.375 -0.615 -0.875 -0.462 -0.500 -0.333 -0.533 -0.833 -0.545 -0.600 -0.286 -0.235 -0.667 -0.500 -0.824 -0.500 -0.308 -0.714 -0.615 -0.286 -0.154 -0.750 -0.750 -0.133 -0.667 -0.471 -0.429 -0.167 -0.571 -0.429 -0.625 -0.154 -0.286 -0.700 -0.308 -0.737 -0.615 -0.815 -0.667 -0.286 diff --git a/sematch/eval/word_similarity/results/type_ws353-wup.txt b/sematch/eval/word_similarity/results/type_ws353-wup.txt deleted file mode 100644 index dd9718f..0000000 --- a/sematch/eval/word_similarity/results/type_ws353-wup.txt +++ /dev/null @@ -1,212 +0,0 @@ -0.357 -0.923 -0.966 -1.000 -0.875 -0.824 -0.632 -0.727 -0.737 -0.167 -0.182 -0.870 -0.545 -0.571 -0.615 -0.556 -0.700 -0.737 -0.815 -0.625 -0.615 -0.800 -0.571 -1.000 -1.000 -0.545 -0.308 -0.167 -0.533 -0.444 -0.625 -0.909 -0.909 -0.286 -0.632 -0.615 -1.000 -1.000 -0.952 -0.909 -0.667 -0.400 -0.952 -0.933 -0.952 -0.105 -0.571 -0.429 -0.667 -0.429 -0.600 -0.667 -0.545 -0.471 -0.933 -0.923 -0.833 -0.727 -0.571 -0.875 -0.286 -0.333 -0.933 -0.929 -0.889 -0.800 -0.800 -0.857 -0.800 -0.533 -0.857 -0.769 -0.632 -0.533 -0.632 -0.762 -0.889 -0.750 -0.769 -0.545 -0.800 -0.667 -0.571 -0.737 -0.966 -0.333 -0.286 -0.308 -0.429 -0.286 -0.824 -0.923 -0.353 -0.714 -0.400 -0.750 -0.167 -0.615 -0.500 -0.235 -0.308 -0.909 -0.500 -0.154 -0.667 -0.533 -0.632 -0.308 -0.615 -0.308 -0.571 -0.714 -0.778 -0.625 -0.667 -0.308 -0.400 -0.533 -0.444 -0.400 -0.267 -0.875 -0.267 -0.286 -0.167 -0.857 -0.857 -0.455 -0.267 -0.714 -0.824 -0.667 -0.308 -0.133 -0.588 -0.615 -0.727 -0.375 -0.133 -0.154 -0.947 -0.625 -0.667 -0.615 -0.947 -0.667 -0.471 -0.375 -0.900 -0.769 -0.500 -0.889 -0.286 -1.000 -0.308 -0.571 -0.500 -0.615 -0.286 -0.143 -0.875 -0.500 -0.462 -0.364 -0.933 -0.222 -0.571 -0.308 -0.857 -0.375 -0.364 -0.909 -0.857 -0.769 -0.375 -0.571 -0.556 -0.545 -0.571 -0.889 -0.857 -0.714 -0.545 -0.667 -0.706 -0.667 -0.235 -0.308 -0.364 -0.778 -0.500 -0.167 -0.545 -0.545 -0.308 -0.286 -0.286 -0.833 -0.533 -0.615 -0.429 -0.200 -0.750 -0.615 -0.800 -0.545 -0.143 -0.625 -0.706 -0.571 -0.500 -0.526 diff --git a/sematch/eval/word_similarity/results_original/graph_mc.txt b/sematch/eval/word_similarity/results_original/graph_mc.txt deleted file mode 100644 index dd3116a..0000000 --- a/sematch/eval/word_similarity/results_original/graph_mc.txt +++ /dev/null @@ -1,28 +0,0 @@ -word1 word2 path lch wup li res res_graph lin jcn wpath wpath_graph -car automobile 1.000 3.638 1.000 1.000 7.591 7.386 1.000 1.000 1.000 1.000 -gem jewel 1.000 3.638 1.000 1.000 12.068 12.876 1.000 1.000 1.000 1.000 -journey voyage 0.500 2.944 0.952 0.819 6.826 9.326 0.778 0.204 0.821 0.889 -boy lad 0.500 2.944 0.947 0.819 8.399 10.796 0.831 0.226 0.867 0.918 -coast shore 0.500 2.944 0.909 0.805 9.416 11.560 0.963 0.582 0.891 0.930 -asylum madhouse 0.500 2.944 0.952 0.819 9.475 10.520 0.856 0.238 0.892 0.913 -magician wizard 1.000 3.638 0.667 1.000 11.981 14.175 1.000 1.000 1.000 1.000 -furnace stove 0.100 1.335 0.526 0.163 2.306 2.729 0.228 0.060 0.157 0.170 -food fruit 0.100 1.335 0.400 0.138 1.593 3.724 0.161 0.084 0.137 0.203 -bird cock 0.500 2.944 0.952 0.819 7.678 9.339 0.774 0.182 0.847 0.889 -bird crane 0.250 2.251 0.870 0.549 7.678 9.339 0.748 0.162 0.649 0.728 -tool implement 0.500 2.944 0.933 0.818 5.877 10.564 0.947 0.604 0.788 0.914 -brother monk 0.500 2.944 0.952 0.819 9.262 8.392 0.986 0.797 0.888 0.867 -lad brother 0.200 2.028 0.667 0.449 2.334 1.540 0.255 0.068 0.296 0.261 -crane implement 0.200 2.028 0.750 0.447 3.258 3.625 0.359 0.079 0.341 0.360 -monk oracle 0.125 1.558 0.571 0.246 2.334 1.540 0.226 0.059 0.194 0.168 -cemetery woodland 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.052 0.143 0.134 -food rooster 0.063 0.865 0.211 0.027 0.802 0.929 0.092 0.059 0.074 0.076 -coast hill 0.200 2.028 0.667 0.425 5.885 5.283 0.599 0.113 0.482 0.448 -forest graveyard 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.052 0.143 0.134 -shore woodland 0.200 2.028 0.600 0.375 1.290 0.955 0.136 0.057 0.250 0.236 -monk slave 0.200 2.028 0.667 0.449 2.334 1.540 0.254 0.068 0.296 0.261 -coast forest 0.167 1.846 0.545 0.307 1.290 2.612 0.131 0.055 0.211 0.198 -lad wizard 0.200 2.028 0.667 0.449 2.334 1.540 0.255 0.068 0.296 0.261 -chord smile 0.091 1.240 0.375 0.113 2.620 4.436 0.246 0.059 0.152 0.212 -glass magician 0.125 1.558 0.471 0.206 2.283 1.532 0.214 0.056 0.192 0.167 -noon string 0.083 1.153 0.267 0.060 0.596 2.612 0.066 0.056 0.094 0.140 diff --git a/sematch/eval/word_similarity/results_original/graph_rg.txt b/sematch/eval/word_similarity/results_original/graph_rg.txt deleted file mode 100644 index 3a0d4c9..0000000 --- a/sematch/eval/word_similarity/results_original/graph_rg.txt +++ /dev/null @@ -1,58 +0,0 @@ -word1 word2 path lch wup li res res_graph lin jcn wpath wpath_graph -gem jewel 1.000 3.638 1.000 1.000 12.068 12.876 1.000 1.000 1.000 1.000 -automobile car 1.000 3.638 1.000 1.000 7.591 7.386 1.000 1.000 1.000 1.000 -cemetery graveyard 1.000 3.638 1.000 1.000 11.288 8.765 1.000 1.000 1.000 1.000 -cushion pillow 0.500 2.944 0.933 0.818 11.288 15.274 0.977 0.650 0.925 0.968 -boy lad 0.500 2.944 0.947 0.819 8.399 10.796 0.831 0.226 0.867 0.918 -cock rooster 1.000 3.638 1.000 1.000 12.163 9.339 1.000 1.000 1.000 1.000 -implement tool 0.500 2.944 0.933 0.818 5.877 10.564 0.947 0.604 0.788 0.914 -forest woodland 1.000 3.638 0.833 0.995 9.614 13.194 1.000 1.000 1.000 1.000 -coast shore 0.500 2.944 0.909 0.805 9.416 11.560 0.963 0.582 0.891 0.930 -autograph signature 0.500 2.944 0.933 0.818 11.632 5.572 0.941 0.409 0.931 0.500 -journey voyage 0.500 2.944 0.952 0.819 6.826 9.326 0.778 0.204 0.821 0.889 -serf slave 0.250 2.251 0.632 0.549 9.090 9.258 0.795 0.176 0.717 0.725 -glass tumbler 0.500 2.944 0.941 0.818 9.442 10.538 0.813 0.188 0.892 0.913 -cord string 0.500 2.944 0.933 0.818 8.472 6.754 0.874 0.291 0.869 0.500 -hill mound 1.000 3.638 1.000 1.000 9.966 15.274 1.000 1.000 1.000 1.000 -magician wizard 1.000 3.638 0.667 1.000 11.981 14.175 1.000 1.000 1.000 1.000 -furnace stove 0.100 1.335 0.526 0.163 2.306 2.729 0.228 0.060 0.157 0.170 -asylum madhouse 0.500 2.944 0.952 0.819 9.475 10.520 0.856 0.238 0.892 0.913 -brother monk 0.500 2.944 0.952 0.819 9.262 8.392 0.986 0.797 0.888 0.867 -food fruit 0.100 1.335 0.400 0.138 1.593 3.724 0.161 0.084 0.137 0.203 -bird cock 0.500 2.944 0.952 0.819 7.678 9.339 0.774 0.182 0.847 0.889 -bird crane 0.250 2.251 0.870 0.549 7.678 9.339 0.748 0.162 0.649 0.728 -oracle sage 0.167 1.846 0.571 0.368 6.513 3.390 0.525 0.078 0.461 0.299 -sage wizard 0.167 1.846 0.632 0.367 2.334 1.540 0.205 0.053 0.252 0.220 -brother lad 0.200 2.028 0.667 0.449 2.334 1.540 0.255 0.068 0.296 0.261 -crane implement 0.200 2.028 0.750 0.447 3.258 3.625 0.359 0.079 0.341 0.360 -magician oracle 0.143 1.692 0.600 0.301 2.334 1.540 0.199 0.051 0.219 0.190 -glass jewel 0.143 1.692 0.588 0.251 2.306 2.729 0.237 0.063 0.217 0.208 -cemetery mound 0.091 1.240 0.375 0.113 1.290 0.955 0.129 0.054 0.118 0.110 -hill woodland 0.167 1.846 0.545 0.307 1.290 0.955 0.141 0.060 0.211 0.198 -crane rooster 0.125 1.558 0.741 0.247 7.678 9.339 0.614 0.094 0.442 0.534 -furnace implement 0.125 1.558 0.588 0.243 2.306 2.729 0.271 0.075 0.193 0.208 -coast hill 0.200 2.028 0.667 0.425 5.885 5.283 0.599 0.113 0.482 0.448 -bird woodland 0.111 1.440 0.400 0.138 1.290 2.612 0.149 0.064 0.130 0.166 -cemetery woodland 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.052 0.143 0.134 -food rooster 0.063 0.865 0.211 0.027 0.802 0.929 0.092 0.059 0.074 0.076 -forest graveyard 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.052 0.143 0.134 -lad wizard 0.200 2.028 0.667 0.449 2.334 1.540 0.255 0.068 0.296 0.261 -mound shore 0.200 2.028 0.667 0.425 5.885 5.283 0.565 0.099 0.482 0.448 -automobile cushion 0.111 1.440 0.600 0.201 3.258 3.625 0.356 0.078 0.205 0.219 -boy sage 0.167 1.846 0.632 0.367 2.334 1.540 0.220 0.057 0.252 0.220 -monk oracle 0.125 1.558 0.571 0.246 2.334 1.540 0.226 0.059 0.194 0.168 -shore woodland 0.200 2.028 0.600 0.375 1.290 0.955 0.136 0.057 0.250 0.236 -coast forest 0.167 1.846 0.545 0.307 1.290 2.612 0.131 0.055 0.211 0.198 -asylum cemetery 0.083 1.153 0.353 0.092 1.290 0.955 0.124 0.052 0.108 0.101 -monk slave 0.200 2.028 0.667 0.449 2.334 1.540 0.254 0.068 0.296 0.261 -cushion jewel 0.143 1.692 0.625 0.296 2.306 2.729 0.223 0.059 0.218 0.235 -boy rooster 0.083 1.153 0.522 0.110 2.224 1.480 0.223 0.061 0.130 0.112 -glass magician 0.125 1.558 0.471 0.206 2.283 1.532 0.214 0.056 0.192 0.167 -graveyard madhouse 0.067 0.930 0.300 0.051 1.290 0.955 0.108 0.045 0.087 0.081 -asylum monk 0.091 1.240 0.444 0.128 1.532 1.227 0.164 0.060 0.123 0.116 -asylum fruit 0.143 1.692 0.625 0.296 2.306 2.729 0.237 0.068 0.218 0.235 -mound stove 0.143 1.692 0.632 0.296 3.258 3.625 0.301 0.071 0.228 0.243 -automobile wizard 0.077 1.073 0.400 0.086 1.532 1.227 0.181 0.067 0.105 0.099 -fruit furnace 0.111 1.440 0.556 0.199 2.306 2.729 0.218 0.061 0.173 0.187 -noon string 0.083 1.153 0.267 0.060 0.596 2.612 0.066 0.056 0.094 0.140 -cord smile 0.091 1.240 0.286 0.073 0.596 2.612 0.054 0.052 0.103 0.152 diff --git a/sematch/eval/word_similarity/results_original/graph_simlex.txt b/sematch/eval/word_similarity/results_original/graph_simlex.txt deleted file mode 100644 index f961ad8..0000000 --- a/sematch/eval/word_similarity/results_original/graph_simlex.txt +++ /dev/null @@ -1,658 +0,0 @@ -word1 word2 path lch wup li res res_graph lin jcn wpath wpath_graph -wife husband 0.333 2.539 0.600 0.670 7.852 8.613 0.883 0.325 0.743 0.774 -book text 0.500 2.944 0.952 0.819 7.349 6.954 0.892 0.359 0.838 0.786 -groom bride 0.333 2.539 0.909 0.670 10.423 15.274 0.917 0.346 0.837 0.938 -night day 0.333 2.539 0.833 0.659 5.886 9.521 0.769 0.220 0.650 0.807 -south north 0.333 2.539 0.875 0.669 7.099 2.743 0.850 0.285 0.709 0.480 -plane airport 0.111 1.440 0.556 0.199 2.306 2.729 0.252 0.068 0.173 0.187 -uncle aunt 0.200 2.028 0.600 0.449 5.710 7.095 0.532 0.091 0.472 0.549 -horse mare 0.500 2.944 0.968 0.819 7.739 10.144 0.804 0.209 0.849 0.906 -bottom top 0.333 2.539 0.900 0.670 8.255 13.194 0.871 0.290 0.759 0.905 -friend buddy 0.500 2.944 0.706 0.818 7.142 6.943 0.866 0.312 0.831 0.825 -student pupil 1.000 3.638 0.667 1.000 7.686 9.725 1.000 1.000 1.000 1.000 -world globe 1.000 3.638 1.000 1.000 7.690 8.119 1.000 1.000 1.000 1.000 -leg arm 0.333 2.539 0.889 0.670 8.054 15.274 0.884 0.322 0.751 0.938 -plane jet 0.500 2.944 0.960 0.819 8.871 11.025 0.894 0.323 0.879 0.921 -woman man 0.333 2.539 0.706 0.670 4.910 4.037 0.787 0.273 0.599 0.552 -horse colt 0.091 1.240 0.583 0.135 4.946 4.834 0.523 0.100 0.232 0.227 -actress actor 0.500 2.944 0.952 0.819 6.547 4.287 0.939 0.541 0.812 0.722 -teacher instructor 1.000 3.638 1.000 1.000 8.890 6.626 1.000 1.000 1.000 1.000 -movie film 1.000 3.638 1.000 1.000 7.817 4.472 1.000 1.000 1.000 1.000 -bird hawk 0.333 2.539 0.909 0.670 7.678 9.339 0.816 0.224 0.735 0.801 -money salary 0.100 1.335 0.471 0.157 4.470 8.737 0.544 0.118 0.232 0.438 -dog cat 0.200 2.028 0.857 0.449 7.912 9.490 0.877 0.310 0.594 0.675 -area region 1.000 3.638 1.000 0.995 7.270 13.664 1.000 1.000 1.000 1.000 -navy army 0.333 2.539 0.889 0.670 7.896 9.382 0.798 0.200 0.744 0.802 -book literature 0.250 2.251 0.769 0.540 4.263 5.519 0.509 0.109 0.463 0.533 -clothes closet 0.125 1.558 0.588 0.243 2.306 2.729 0.245 0.066 0.193 0.208 -sunset sunrise 0.333 2.539 0.900 0.670 8.087 15.274 0.715 0.147 0.752 0.938 -child adult 0.333 2.539 0.750 0.669 2.334 1.540 0.397 0.124 0.457 0.413 -cow cattle 0.500 2.944 0.971 0.819 8.593 13.887 0.895 0.331 0.872 0.957 -book story 0.200 2.028 0.714 0.442 5.646 7.501 0.704 0.174 0.468 0.571 -winter summer 0.333 2.539 0.857 0.667 7.905 12.383 0.816 0.219 0.745 0.888 -taxi cab 1.000 3.638 1.000 1.000 10.882 13.328 1.000 1.000 1.000 1.000 -tree maple 0.333 2.539 0.909 0.670 7.765 7.401 0.771 0.178 0.739 0.723 -bed bedroom 0.167 1.846 0.667 0.362 3.979 4.386 0.413 0.081 0.258 0.275 -roof ceiling 1.000 3.638 1.000 1.000 9.645 2.729 1.000 1.000 1.000 1.000 -disease infection 0.250 2.251 0.842 0.549 6.715 7.004 0.743 0.177 0.599 0.614 -arm shoulder 0.333 2.539 0.875 0.669 6.866 14.581 0.735 0.168 0.698 0.928 -sheep lamb 0.200 2.028 0.667 0.449 4.946 4.834 0.457 0.078 0.296 0.261 -lady gentleman 0.200 2.028 0.600 0.449 4.910 4.037 0.495 0.091 0.428 0.381 -boat anchor 0.111 1.440 0.600 0.201 3.258 3.625 0.321 0.068 0.205 0.219 -priest monk 0.143 1.692 0.600 0.301 2.334 1.540 0.267 0.072 0.219 0.190 -toe finger 0.333 2.539 0.889 0.670 8.924 15.274 0.883 0.296 0.786 0.938 -river stream 0.500 2.944 0.909 0.805 6.762 5.575 0.986 0.841 0.819 0.776 -anger fury 0.500 2.944 0.933 0.818 8.418 14.581 0.886 0.315 0.867 0.963 -date calendar 0.111 1.440 0.400 0.138 2.855 3.353 0.279 0.064 0.174 0.190 -sea ocean 1.000 3.638 1.000 0.995 9.598 5.070 1.000 1.000 1.000 1.000 -second minute 1.000 3.638 1.000 0.999 7.712 9.521 1.000 1.000 1.000 1.000 -hand thumb 0.250 2.251 0.824 0.548 6.389 13.077 0.673 0.139 0.581 0.860 -wood log 0.500 2.944 0.941 0.818 8.502 12.709 0.855 0.258 0.870 0.945 -mud dirt 0.500 2.944 0.941 0.818 8.311 10.930 0.930 0.446 0.865 0.920 -hallway corridor 0.500 2.944 0.947 0.819 9.167 11.777 0.983 0.761 0.886 0.933 -way manner 1.000 3.638 1.000 0.984 6.652 9.413 1.000 1.000 1.000 1.000 -mouse cat 0.200 2.028 0.815 0.449 5.414 7.651 0.527 0.093 0.401 0.524 -cop sheriff 0.250 2.251 0.870 0.549 7.723 6.704 0.734 0.151 0.651 0.598 -death burial 0.125 1.558 0.533 0.233 2.332 4.982 0.223 0.061 0.184 0.255 -music melody 0.500 2.944 0.909 0.805 6.460 12.441 0.876 0.406 0.760 0.775 -beer alcohol 0.333 2.539 0.900 0.670 7.585 8.865 0.887 0.341 0.731 0.783 -mouth lip 1.000 3.638 1.000 1.000 9.167 7.536 1.000 1.000 1.000 1.000 -storm hurricane 0.250 2.251 0.842 0.549 10.322 10.943 0.898 0.298 0.769 0.793 -tax income 0.100 1.335 0.471 0.157 4.470 8.737 0.602 0.145 0.232 0.438 -flower violet 0.143 1.692 0.727 0.301 6.028 6.755 0.597 0.109 0.390 0.429 -paper cardboard 0.500 2.944 0.933 0.818 5.855 12.329 0.722 0.181 0.787 0.940 -floor ceiling 0.333 2.539 0.923 0.670 8.031 10.564 0.867 0.288 0.750 0.679 -beach seashore 0.250 2.251 0.727 0.520 5.885 5.283 0.577 0.104 0.553 0.520 -rod curtain 0.200 2.028 0.750 0.447 3.258 3.625 0.357 0.078 0.341 0.360 -hound fox 0.200 2.028 0.867 0.449 8.886 10.383 0.761 0.152 0.645 0.717 -street alley 0.500 2.944 0.947 0.819 8.670 8.139 0.828 0.217 0.874 0.860 -boat deck 0.167 1.846 0.737 0.367 5.206 6.699 0.503 0.089 0.390 0.471 -car horn 0.167 1.846 0.706 0.366 3.258 3.625 0.359 0.079 0.293 0.310 -friend guest 0.200 2.028 0.667 0.449 2.334 1.540 0.275 0.075 0.296 0.261 -employer employee 0.200 2.028 0.667 0.449 2.334 1.540 0.292 0.081 0.296 0.261 -hand wrist 0.125 1.558 0.588 0.243 4.402 7.536 0.468 0.091 0.276 0.434 -ball cannon 0.250 2.251 0.783 0.540 6.763 8.747 0.608 0.103 0.475 0.642 -alcohol brandy 0.333 2.539 0.900 0.670 7.585 8.865 0.782 0.191 0.731 0.783 -victory triumph 1.000 3.638 1.000 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2.251 0.880 0.549 7.995 5.949 0.801 0.201 0.665 0.557 -oak maple 0.333 2.539 0.889 0.670 8.502 12.709 0.702 0.122 0.769 0.895 -bee queen 0.250 2.251 0.870 0.549 7.809 6.746 0.750 0.161 0.656 0.600 -car bicycle 0.333 2.539 0.800 0.670 6.452 6.902 0.766 0.203 0.678 0.700 -goal quest 0.143 1.692 0.571 0.285 2.645 5.359 0.311 0.079 0.231 0.355 -august month 0.333 2.539 0.857 0.667 6.530 12.501 0.782 0.216 0.682 0.891 -army squad 0.200 2.028 0.750 0.447 4.752 5.607 0.565 0.120 0.419 0.466 -cloud weather 0.333 2.539 0.875 0.669 6.561 10.819 0.772 0.205 0.684 0.848 -physician doctor 1.000 3.638 1.000 1.000 8.052 5.979 1.000 1.000 1.000 1.000 -canyon valley 0.333 2.539 0.857 0.667 9.104 8.786 0.887 0.301 0.792 0.780 -river valley 0.111 1.440 0.333 0.108 0.802 0.929 0.100 0.065 0.130 0.133 -sun sky 0.091 1.240 0.286 0.073 0.802 0.929 0.087 0.056 0.107 0.110 -target arrow 0.125 1.558 0.600 0.233 4.508 7.405 0.438 0.080 0.281 0.427 -chocolate pie 0.200 2.028 0.714 0.442 6.109 8.084 0.525 0.083 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1.153 0.353 0.092 3.337 5.761 0.333 0.070 0.161 0.247 -helper maid 0.200 2.028 0.600 0.449 4.518 3.622 0.538 0.114 0.407 0.359 -leader manager 0.333 2.539 0.667 0.670 4.969 3.258 0.707 0.196 0.602 0.508 -lemon tea 0.125 1.558 0.720 0.247 6.538 7.385 0.571 0.092 0.381 0.426 -bee ant 0.333 2.539 0.917 0.670 10.096 9.472 0.909 0.332 0.826 0.805 -basketball baseball 0.333 2.539 0.900 0.670 8.716 15.274 0.816 0.203 0.771 0.938 -rice bean 0.143 1.692 0.750 0.301 7.508 8.228 0.657 0.113 0.471 0.511 -bed furniture 0.333 2.539 0.889 0.670 6.616 10.855 0.886 0.370 0.686 0.849 -emotion passion 0.333 2.539 0.833 0.659 5.073 11.808 0.680 0.173 0.608 0.875 -anarchy chaos 0.250 2.251 0.769 0.540 8.531 8.942 0.712 0.127 0.691 0.710 -crime violation 0.500 2.944 0.947 0.819 7.427 10.174 0.821 0.236 0.840 0.906 -machine engine 0.333 2.539 0.889 0.670 7.076 7.743 0.854 0.297 0.701 0.738 -beach sea 0.143 1.692 0.400 0.162 0.802 0.929 0.081 0.052 0.166 0.170 -alley bowl 0.143 1.692 0.625 0.296 2.306 2.729 0.215 0.056 0.218 0.235 -jar bottle 0.333 2.539 0.889 0.670 7.143 10.689 0.699 0.140 0.711 0.844 -strength capability 0.500 2.944 0.923 0.815 8.752 9.379 0.963 0.601 0.876 0.670 -seed mustard 0.100 1.335 0.571 0.164 3.337 5.761 0.337 0.071 0.174 0.266 -guitar drum 0.200 2.028 0.800 0.449 7.544 10.211 0.674 0.120 0.574 0.709 -opinion idea 0.500 2.944 0.933 0.818 6.625 13.664 0.868 0.332 0.814 0.955 -north west 0.333 2.539 0.875 0.669 7.099 2.743 0.816 0.238 0.709 0.480 -diet salad 0.167 1.846 0.667 0.362 5.279 7.383 0.453 0.073 0.394 0.510 -mother wife 0.250 2.251 0.571 0.549 6.257 7.095 0.724 0.173 0.574 0.553 -dad mother 0.250 2.251 0.889 0.549 7.519 9.162 0.842 0.261 0.641 0.720 -captain sailor 0.250 2.251 0.870 0.549 7.393 7.277 0.889 0.351 0.634 0.628 -meter yard 0.250 2.251 0.800 0.546 6.061 13.887 0.662 0.139 0.563 0.881 -beer champagne 0.167 1.846 0.783 0.368 7.585 8.865 0.711 0.139 0.521 0.591 -motor boat 0.143 1.692 0.667 0.300 3.258 3.625 0.372 0.083 0.256 0.272 -card bridge 0.200 2.028 0.818 0.449 8.206 11.403 0.881 0.310 0.609 0.761 -science psychology 0.500 2.944 0.941 0.818 7.187 8.052 0.847 0.279 0.833 0.858 -sinner saint 0.167 1.846 0.632 0.367 2.334 1.540 0.214 0.055 0.252 0.220 -destruction construction 0.111 1.440 0.526 0.191 2.332 4.982 0.267 0.078 0.165 0.252 -crowd bunch 1.000 3.638 1.000 0.995 9.482 6.726 1.000 1.000 1.000 1.000 -beach reef 0.200 2.028 0.667 0.425 5.885 5.283 0.594 0.110 0.482 0.448 -man child 0.333 2.539 0.750 0.669 2.334 1.540 0.368 0.111 0.457 0.413 -bread cheese 0.250 2.251 0.750 0.546 6.880 8.826 0.644 0.116 0.607 0.705 -champion winner 0.250 2.251 0.706 0.549 5.395 2.850 0.495 0.083 0.526 0.386 -celebration ceremony 0.333 2.539 0.857 0.667 6.583 9.528 0.663 0.130 0.685 0.807 -menu order 0.125 1.558 0.533 0.233 3.848 6.513 0.400 0.080 0.252 0.379 -king princess 0.167 1.846 0.600 0.368 4.969 3.258 0.454 0.077 0.377 0.293 -wealth prestige 0.143 1.692 0.571 0.285 3.140 6.399 0.305 0.065 0.251 0.410 -endurance strength 0.500 2.944 0.909 0.805 7.143 8.527 0.873 0.326 0.831 0.626 -danger threat 0.500 2.944 0.889 0.775 8.956 12.329 0.931 0.428 0.881 0.940 -god priest 0.200 2.028 0.600 0.449 4.969 3.258 0.591 0.127 0.431 0.341 -men fraternity 0.200 2.028 0.667 0.375 3.907 3.505 0.397 0.078 0.324 0.346 -buddy companion 0.333 2.539 0.667 0.670 7.142 6.943 0.797 0.216 0.711 0.702 -teacher helper 0.143 1.692 0.600 0.301 2.334 1.540 0.309 0.088 0.219 0.190 -body stomach 0.333 2.539 0.833 0.659 4.402 7.536 0.470 0.092 0.572 0.729 -tongue throat 0.200 2.028 0.714 0.442 4.402 7.536 0.416 0.075 0.400 0.573 -house carpet 0.143 1.692 0.625 0.296 2.306 2.729 0.258 0.070 0.218 0.235 -intelligence skill 0.333 2.539 0.833 0.659 4.861 6.634 0.575 0.122 0.597 0.687 -journey conquest 0.111 1.440 0.600 0.201 3.734 6.132 0.446 0.097 0.223 0.329 -god prey 0.143 1.692 0.667 0.301 2.334 1.540 0.250 0.067 0.219 0.190 -brother soul 0.333 2.539 0.750 0.669 2.334 1.540 0.400 0.125 0.457 0.413 -adversary opponent 1.000 3.638 0.750 1.000 9.006 9.430 1.000 1.000 1.000 1.000 -death catastrophe 0.200 2.028 0.714 0.442 4.936 10.318 0.481 0.100 0.429 0.714 -monster demon 1.000 3.638 1.000 1.000 10.534 13.077 1.000 1.000 1.000 1.000 -day morning 0.333 2.539 0.833 0.659 3.800 7.890 0.484 0.110 0.539 0.744 -man victor 0.250 2.251 0.706 0.548 2.334 1.540 0.311 0.088 0.359 0.320 -friend guy 0.200 2.028 0.667 0.449 2.334 1.540 0.276 0.076 0.296 0.261 -song story 0.167 1.846 0.545 0.307 2.620 4.436 0.338 0.089 0.264 0.350 -ray sunshine 0.333 2.539 0.917 0.670 7.782 13.482 0.801 0.206 0.740 0.910 -guy stud 0.333 2.539 0.900 0.670 7.381 10.132 0.658 0.115 0.667 0.827 -chicken rice 0.143 1.692 0.600 0.301 3.337 5.761 0.315 0.065 0.219 0.247 -box elevator 0.167 1.846 0.706 0.366 3.258 3.625 0.333 0.071 0.293 0.310 -butter potato 0.200 2.028 0.750 0.447 6.880 8.826 0.644 0.116 0.537 0.642 -apartment furniture 0.143 1.692 0.625 0.296 2.306 2.729 0.299 0.085 0.218 0.235 -lake swamp 0.125 1.558 0.364 0.132 0.802 2.612 0.077 0.049 0.146 0.150 -salad vinegar 0.111 1.440 0.556 0.199 5.279 7.383 0.432 0.067 0.289 0.394 -flower bulb 0.200 2.028 0.778 0.449 6.394 9.625 0.619 0.113 0.510 0.682 -cloud mist 0.250 2.251 0.824 0.548 9.114 13.887 0.879 0.284 0.718 0.881 -driver pilot 0.200 2.028 0.609 0.449 5.268 9.016 0.512 0.091 0.407 0.454 -sugar honey 0.333 2.539 0.900 0.670 10.355 11.636 0.927 0.380 0.834 0.870 -body shoulder 0.333 2.539 0.833 0.659 4.402 7.536 0.479 0.094 0.572 0.729 -idea image 0.250 2.251 0.769 0.540 3.292 5.771 0.533 0.147 0.410 0.547 -father brother 0.200 2.028 0.667 0.449 5.710 7.095 0.639 0.134 0.417 0.493 -moon planet 0.250 2.251 0.800 0.546 6.932 8.564 0.781 0.204 0.610 0.693 -ball costume 0.143 1.692 0.625 0.296 2.306 2.729 0.235 0.063 0.218 0.235 -rail fence 0.333 2.539 0.889 0.670 7.608 7.446 0.798 0.206 0.732 0.725 -room bed 0.200 2.028 0.714 0.442 3.979 4.386 0.531 0.125 0.295 0.315 -flower bush 0.167 1.846 0.762 0.368 6.028 8.119 0.794 0.242 0.434 0.475 -bone knee 0.143 1.692 0.625 0.296 4.402 7.536 0.475 0.093 0.308 0.472 -arm knee 0.333 2.539 0.875 0.669 6.866 14.581 0.720 0.158 0.698 0.928 -bottom side 0.500 2.944 0.947 0.819 6.825 13.194 0.872 0.333 0.821 0.950 -vessel vein 0.333 2.539 0.889 0.670 9.468 9.071 0.928 0.405 0.805 0.791 -cat rabbit 0.143 1.692 0.786 0.301 5.414 7.651 0.517 0.090 0.358 0.479 -meat sandwich 0.100 1.335 0.400 0.138 3.337 5.761 0.440 0.105 0.190 0.287 -belief concept 0.333 2.539 0.857 0.667 4.004 5.984 0.700 0.261 0.550 0.655 -intelligence insight 0.200 2.028 0.667 0.425 2.645 5.359 0.337 0.088 0.311 0.453 -attention interest 0.200 2.028 0.750 0.447 3.144 5.951 0.404 0.097 0.335 0.461 -attitude confidence 0.125 1.558 0.462 0.206 2.298 5.951 0.273 0.076 0.193 0.350 -right justice 0.500 2.944 0.857 0.819 7.990 10.917 0.988 0.838 0.856 0.920 -argument agreement 0.250 2.251 0.778 0.540 5.679 9.326 0.699 0.170 0.501 0.667 -depth magnitude 0.333 2.539 0.833 0.659 5.267 10.081 0.709 0.188 0.618 0.826 -medium news 0.143 1.692 0.533 0.251 2.298 5.951 0.227 0.063 0.218 0.386 -winner candidate 0.333 2.539 0.750 0.669 2.334 1.540 0.269 0.073 0.457 0.413 -birthday date 0.500 2.944 0.923 0.815 9.295 14.581 0.886 0.294 0.888 0.963 -fee payment 0.200 2.028 0.778 0.449 5.973 10.679 0.770 0.219 0.487 0.730 -bible hymn 0.100 1.335 0.400 0.138 2.620 4.436 0.276 0.068 0.166 0.230 -exit doorway 0.143 1.692 0.625 0.296 2.306 2.729 0.263 0.072 0.218 0.235 -man sentry 0.143 1.692 0.600 0.301 2.334 1.540 0.299 0.084 0.219 0.190 -aisle hall 0.250 2.251 0.842 0.549 7.451 9.176 0.677 0.123 0.637 0.721 -whiskey gin 0.333 2.539 0.909 0.670 8.856 12.229 0.863 0.262 0.783 0.884 -blood marrow 0.125 1.558 0.545 0.138 3.337 5.761 0.317 0.072 0.190 0.287 -oil mink 0.100 1.335 0.571 0.164 5.118 9.717 0.451 0.074 0.240 0.466 -floor deck 0.250 2.251 0.824 0.548 6.854 10.649 0.729 0.164 0.606 0.782 -roof floor 0.333 2.539 0.923 0.670 8.031 5.359 0.867 0.288 0.750 0.333 -door floor 0.333 2.539 0.889 0.670 6.550 9.580 0.722 0.165 0.626 0.758 -shoulder head 0.250 2.251 0.769 0.540 6.133 9.055 0.679 0.147 0.471 0.642 -wagon carriage 0.333 2.539 0.800 0.670 6.452 6.902 0.688 0.146 0.678 0.700 -car carriage 0.500 2.944 0.952 0.819 8.094 12.383 0.875 0.302 0.859 0.941 -elbow ankle 0.200 2.028 0.778 0.449 8.886 15.274 0.764 0.154 0.645 0.883 -wealth fame 0.125 1.558 0.533 0.233 3.140 6.399 0.303 0.065 0.224 0.373 -sorrow shame 0.250 2.251 0.769 0.540 5.073 11.808 0.515 0.095 0.508 0.823 -administration management 0.500 2.944 0.941 0.818 7.380 12.278 0.961 0.628 0.838 0.939 -communication conversation 0.250 2.251 0.667 0.458 2.620 4.436 0.501 0.161 0.374 0.473 -pollution atmosphere 0.250 2.251 0.769 0.540 4.235 6.780 0.427 0.081 0.462 0.602 -anatomy biology 0.333 2.539 0.917 0.670 11.000 8.121 0.950 0.462 0.853 0.754 -college profession 0.250 2.251 0.769 0.540 5.967 7.737 0.652 0.136 0.558 0.652 -book topic 0.200 2.028 0.667 0.425 3.848 6.513 0.517 0.122 0.371 0.517 -formula equation 0.333 2.539 0.857 0.667 8.905 11.179 0.901 0.338 0.785 0.858 -book information 0.333 2.539 0.833 0.659 5.646 7.853 0.820 0.287 0.638 0.743 -boy partner 0.250 2.251 0.706 0.548 5.710 7.095 0.729 0.191 0.417 0.493 -sky universe 0.100 1.335 0.308 0.089 0.802 0.929 0.097 0.063 0.117 0.120 -population people 0.500 2.944 0.889 0.775 5.371 7.884 0.722 0.194 0.768 0.853 -college class 0.200 2.028 0.667 0.425 3.907 3.505 0.452 0.100 0.345 0.350 -chief mayor 0.250 2.251 0.632 0.549 4.969 3.258 0.582 0.123 0.503 0.408 -rabbi minister 0.250 2.251 0.571 0.549 6.962 5.402 0.650 0.117 0.612 0.527 -meter inch 0.250 2.251 0.800 0.546 6.061 13.887 0.669 0.143 0.563 0.881 -polyester cotton 0.333 2.539 0.857 0.667 6.654 12.016 0.570 0.091 0.688 0.880 -lawyer banker 0.167 1.846 0.632 0.367 2.334 1.540 0.238 0.063 0.252 0.220 -violin instrument 0.250 2.251 0.842 0.549 7.544 10.211 0.782 0.192 0.642 0.765 -camp cabin 0.200 2.028 0.778 0.449 6.484 6.016 0.617 0.111 0.515 0.489 -pot appliance 0.200 2.028 0.750 0.447 4.076 6.754 0.376 0.085 0.341 0.474 -linen fabric 0.500 2.944 0.923 0.815 6.654 12.016 0.689 0.142 0.815 0.936 -whiskey champagne 0.167 1.846 0.783 0.368 7.585 8.865 0.706 0.137 0.521 0.591 -girl child 0.333 2.539 0.909 0.670 6.838 8.082 0.900 0.398 0.697 0.752 -cottage cabin 0.333 2.539 0.800 0.670 7.279 6.036 0.649 0.113 0.717 0.658 -bird hen 0.500 2.944 0.952 0.819 9.123 13.887 0.896 0.320 0.847 0.889 -racket noise 1.000 3.638 1.000 1.000 10.554 13.887 1.000 1.000 1.000 1.000 -sunset evening 0.100 1.335 0.308 0.089 0.596 2.612 0.064 0.054 0.113 0.166 -drizzle rain 0.500 2.944 0.952 0.819 9.560 0.929 0.853 0.233 0.894 0.500 -adult baby 0.333 2.539 0.778 0.669 4.946 4.834 0.683 0.180 0.499 0.451 -charcoal coal 0.250 2.251 0.769 0.540 7.823 12.566 0.712 0.136 0.656 0.846 -body spine 0.250 2.251 0.769 0.540 4.402 7.536 0.536 0.116 0.471 0.642 -head nail 0.333 2.539 0.857 0.667 6.061 13.887 0.612 0.115 0.659 0.917 -log timber 0.125 1.558 0.667 0.246 4.076 6.754 0.360 0.064 0.262 0.392 -spoon cup 0.333 2.539 0.833 0.670 7.490 8.640 0.695 0.132 0.727 0.775 -body nerve 0.143 1.692 0.625 0.296 4.402 7.536 0.452 0.086 0.308 0.472 -man husband 0.200 2.028 0.667 0.449 6.547 8.232 0.808 0.243 0.519 0.611 -bone neck 0.143 1.692 0.625 0.296 4.402 7.536 0.476 0.094 0.308 0.472 -frustration anger 0.333 2.539 0.875 0.669 8.418 14.581 0.834 0.230 0.766 0.928 -river sea 0.250 2.251 0.727 0.520 6.019 5.070 0.732 0.185 0.561 0.508 -task job 1.000 3.638 1.000 1.000 8.464 8.647 1.000 1.000 1.000 1.000 -club society 1.000 3.638 1.000 0.999 7.735 5.523 1.000 1.000 1.000 1.000 -reflection image 0.500 2.944 0.941 0.818 8.170 12.971 0.861 0.275 0.861 0.948 -prince king 0.167 1.846 0.600 0.368 4.969 3.258 0.528 0.101 0.377 0.293 -snow weather 0.333 2.539 0.889 0.670 6.729 12.709 0.799 0.228 0.692 0.895 -people party 0.200 2.028 0.600 0.375 3.336 3.440 0.444 0.123 0.321 0.346 -boy brother 0.200 2.028 0.667 0.449 5.710 7.095 0.660 0.145 0.338 0.410 -root grass 0.167 1.846 0.632 0.367 2.334 1.540 0.281 0.081 0.252 0.220 -brow eye 0.167 1.846 0.667 0.362 4.402 7.536 0.456 0.087 0.348 0.518 -money pearl 0.091 1.240 0.286 0.073 0.596 2.612 0.066 0.056 0.103 0.152 -money diamond 0.100 1.335 0.400 0.138 3.037 5.511 0.302 0.067 0.180 0.275 -vehicle bus 0.250 2.251 0.824 0.548 5.922 6.108 0.722 0.180 0.547 0.565 -cab bus 0.333 2.539 0.923 0.670 7.591 7.386 0.711 0.139 0.731 0.722 -house barn 0.250 2.251 0.824 0.548 5.715 4.753 0.628 0.129 0.544 0.490 -finger palm 0.333 2.539 0.857 0.667 6.061 13.887 0.572 0.099 0.659 0.917 -car bridge 0.167 1.846 0.706 0.366 3.979 4.386 0.442 0.091 0.327 0.347 -effort difficulty 0.500 2.944 0.947 0.819 7.078 14.581 0.892 0.369 0.829 0.963 -fact insight 0.200 2.028 0.667 0.425 3.140 6.399 0.375 0.102 0.311 0.453 -job management 0.250 2.251 0.824 0.548 5.911 10.659 0.754 0.206 0.555 0.782 -cancer sickness 0.200 2.028 0.818 0.449 7.264 7.037 0.793 0.208 0.558 0.546 -word newspaper 0.143 1.692 0.571 0.285 3.611 5.572 0.442 0.099 0.272 0.366 -composer writer 0.143 1.692 0.600 0.301 2.334 1.540 0.346 0.102 0.219 0.190 -actor singer 0.250 2.251 0.667 0.549 5.718 3.641 0.820 0.286 0.544 0.429 -shelter hut 0.500 2.944 0.933 0.818 8.218 12.016 0.848 0.253 0.862 0.936 -bathroom kitchen 0.333 2.539 0.889 0.670 5.408 8.216 0.660 0.152 0.626 0.758 -cabin hut 0.167 1.846 0.706 0.366 3.979 4.386 0.358 0.065 0.327 0.347 -door kitchen 0.333 2.539 0.889 0.670 5.408 8.216 0.582 0.114 0.626 0.758 -value belief 0.250 2.251 0.800 0.546 4.004 5.984 0.525 0.136 0.449 0.559 -wisdom intelligence 0.250 2.251 0.769 0.540 4.861 6.634 0.631 0.149 0.497 0.594 -ignorance intelligence 0.200 2.028 0.667 0.425 2.645 5.359 0.311 0.078 0.311 0.453 -happiness luck 0.200 2.028 0.667 0.425 3.140 6.399 0.383 0.090 0.335 0.510 -idea scheme 0.333 2.539 0.857 0.667 4.004 5.984 0.656 0.192 0.550 0.655 -mood emotion 0.333 2.539 0.833 0.659 5.073 11.808 0.642 0.150 0.608 0.875 -happiness peace 0.200 2.028 0.714 0.442 5.073 11.808 0.540 0.104 0.437 0.777 -despair misery 0.250 2.251 0.769 0.540 5.073 11.808 0.467 0.079 0.508 0.823 -logic arithmetic 0.125 1.558 0.667 0.246 5.589 7.972 0.465 0.072 0.332 0.458 -denial confession 0.250 2.251 0.800 0.546 7.613 12.566 0.687 0.126 0.646 0.846 -argument criticism 0.167 1.846 0.615 0.348 4.385 9.003 0.506 0.104 0.321 0.461 -aggression hostility 1.000 3.638 1.000 0.999 11.098 11.040 1.000 1.000 1.000 1.000 -hysteria confusion 0.143 1.692 0.625 0.296 5.073 11.808 0.439 0.082 0.341 0.699 -chemistry theory 0.125 1.558 0.588 0.243 3.292 5.771 0.349 0.075 0.229 0.341 -trial verdict 0.167 1.846 0.737 0.367 6.685 12.635 0.631 0.114 0.471 0.770 -comfort safety 0.333 2.539 0.833 0.659 4.235 6.780 0.483 0.099 0.563 0.694 -confidence self 0.200 2.028 0.778 0.449 6.000 10.368 0.583 0.104 0.488 0.717 -vision perception 0.333 2.539 0.875 0.669 6.460 12.441 0.770 0.206 0.679 0.889 -era decade 0.333 2.539 0.833 0.659 3.800 7.890 0.468 0.104 0.539 0.744 -biography fiction 0.100 1.335 0.400 0.138 2.620 4.436 0.341 0.090 0.166 0.230 -discussion argument 0.500 2.944 0.923 0.815 7.114 12.971 0.846 0.278 0.830 0.948 -code symbol 0.200 2.028 0.600 0.375 2.620 4.436 0.463 0.141 0.310 0.402 -danger disease 0.143 1.692 0.625 0.296 4.235 6.780 0.488 0.101 0.300 0.431 -accident catastrophe 0.250 2.251 0.824 0.548 7.958 10.391 0.836 0.243 0.663 0.772 -journey trip 0.500 2.944 0.952 0.819 6.826 9.326 0.917 0.446 0.821 0.889 -activity movement 0.200 2.028 0.750 0.447 5.841 13.328 0.768 0.221 0.479 0.830 -gossip news 0.111 1.440 0.471 0.168 2.620 4.436 0.268 0.065 0.183 0.252 -father god 0.250 2.251 0.842 0.549 7.035 8.997 0.879 0.340 0.616 0.598 -action course 0.500 2.944 0.923 0.815 3.734 6.132 0.669 0.213 0.697 0.797 -fever illness 0.100 1.335 0.471 0.157 3.140 6.399 0.315 0.068 0.183 0.317 -aviation flight 0.500 2.944 0.952 0.819 8.435 9.151 0.968 0.643 0.868 0.658 -game action 0.250 2.251 0.800 0.546 3.258 5.519 0.436 0.142 0.402 0.533 -molecule air 0.200 2.028 0.571 0.442 3.886 6.149 0.422 0.086 0.373 0.496 -home state 0.250 2.251 0.800 0.546 4.752 6.399 0.685 0.186 0.490 0.582 -word literature 0.250 2.251 0.769 0.540 4.263 5.771 0.494 0.103 0.463 0.533 -adult guardian 0.250 2.251 0.706 0.548 2.334 1.540 0.390 0.120 0.359 0.320 -newspaper information 0.100 1.335 0.400 0.138 2.855 3.353 0.332 0.080 0.174 0.190 -cousin uncle 0.250 2.251 0.632 0.549 5.710 7.095 0.551 0.097 0.544 0.619 -author reader 0.200 2.028 0.667 0.449 2.334 1.540 0.304 0.085 0.296 0.261 -guy partner 0.200 2.028 0.667 0.449 2.334 1.540 0.265 0.072 0.296 0.261 -area corner 0.500 2.944 0.933 0.818 5.352 8.134 0.714 0.189 0.768 0.860 -ballad song 0.500 2.944 0.933 0.818 8.298 5.896 0.934 0.459 0.864 0.788 -wall decoration 0.250 2.251 0.769 0.540 2.306 2.729 0.302 0.086 0.358 0.380 -word page 0.111 1.440 0.500 0.191 3.611 5.572 0.458 0.105 0.219 0.302 -nurse scientist 0.200 2.028 0.667 0.449 2.334 1.540 0.276 0.076 0.296 0.261 -politician president 0.250 2.251 0.632 0.549 4.969 3.258 0.663 0.165 0.503 0.408 -president mayor 0.200 2.028 0.600 0.449 4.969 3.258 0.529 0.102 0.431 0.341 -book essay 0.250 2.251 0.769 0.540 4.263 5.519 0.557 0.129 0.463 0.533 -man warrior 0.333 2.539 0.750 0.669 2.334 1.540 0.294 0.082 0.457 0.413 -article journal 0.250 2.251 0.769 0.540 4.583 5.519 0.512 0.103 0.463 0.533 -breakfast supper 0.333 2.539 0.875 0.669 8.233 12.278 0.771 0.170 0.758 0.886 -crowd parade 0.143 1.692 0.500 0.251 2.855 3.353 0.290 0.067 0.240 0.260 -aisle hallway 0.250 2.251 0.842 0.549 7.451 9.176 0.677 0.123 0.637 0.721 -teacher rabbi 0.125 1.558 0.571 0.246 2.334 2.612 0.226 0.059 0.194 0.168 -hip lip 0.250 2.251 0.769 0.540 6.247 9.607 0.576 0.098 0.471 0.642 -book article 0.333 2.539 0.857 0.667 5.958 6.954 0.715 0.174 0.654 0.702 -room cell 0.500 2.944 0.941 0.818 5.408 8.216 0.804 0.276 0.770 0.862 -box booth 0.200 2.028 0.800 0.449 6.616 10.855 0.586 0.097 0.523 0.738 -daughter kid 0.333 2.539 0.909 0.670 6.838 8.082 0.900 0.398 0.697 0.752 -limb leg 0.500 2.944 0.941 0.818 8.054 15.274 0.935 0.470 0.858 0.968 -liver lung 0.250 2.251 0.824 0.548 8.558 0.929 0.756 0.153 0.692 0.250 -classroom hallway 0.100 1.335 0.526 0.163 2.306 2.729 0.223 0.059 0.157 0.170 -mountain ledge 0.250 2.251 0.769 0.540 6.949 5.558 0.664 0.124 0.611 0.535 -car elevator 0.143 1.692 0.667 0.300 3.258 3.625 0.353 0.077 0.256 0.272 -bed couch 0.500 2.944 0.952 0.819 8.319 15.274 0.823 0.218 0.865 0.968 -clothes button 0.200 2.028 0.714 0.442 2.306 2.729 0.242 0.065 0.295 0.315 -clothes coat 0.250 2.251 0.800 0.546 6.125 9.840 0.654 0.134 0.511 0.745 -kidney organ 0.250 2.251 0.800 0.546 6.428 9.484 0.695 0.151 0.583 0.735 -apple sauce 0.077 1.073 0.333 0.076 3.337 5.761 0.315 0.064 0.149 0.232 -chicken steak 0.167 1.846 0.706 0.366 6.725 10.582 0.614 0.106 0.473 0.680 -car hose 0.111 1.440 0.556 0.199 2.306 2.729 0.245 0.066 0.173 0.187 -tobacco cigarette 0.333 2.539 0.800 0.670 9.104 13.664 0.977 0.701 0.792 0.913 -student professor 0.125 1.558 0.571 0.246 2.334 1.540 0.276 0.076 0.194 0.168 -baby daughter 0.250 2.251 0.870 0.549 6.838 8.082 0.754 0.183 0.605 0.669 -pipe cigar 0.067 0.930 0.222 0.033 0.802 2.612 0.081 0.052 0.075 0.113 -milk juice 0.333 2.539 0.875 0.669 7.061 9.573 0.643 0.116 0.707 0.809 -box cigar 0.071 0.999 0.235 0.040 0.802 2.612 0.078 0.050 0.084 0.121 -apartment hotel 0.200 2.028 0.750 0.447 3.979 4.386 0.463 0.098 0.378 0.399 -cup cone 0.250 2.251 0.824 0.548 6.394 9.625 0.583 0.098 0.581 0.741 -horse ox 0.111 1.440 0.750 0.202 7.024 9.231 0.723 0.157 0.375 0.495 -throat nose 0.250 2.251 0.800 0.546 6.676 13.077 0.650 0.122 0.597 0.860 -bone teeth 0.500 2.944 0.947 0.819 8.435 9.451 0.945 0.505 0.868 0.892 -bone elbow 0.143 1.692 0.625 0.296 4.402 7.536 0.439 0.082 0.308 0.472 -bacon bean 0.111 1.440 0.600 0.201 6.109 8.084 0.524 0.083 0.328 0.432 -cup jar 0.333 2.539 0.833 0.659 6.440 7.001 0.585 0.099 0.678 0.598 -proof fact 0.250 2.251 0.769 0.540 5.081 9.032 0.654 0.157 0.509 0.714 -appointment engagement 1.000 3.638 1.000 1.000 9.251 9.560 1.000 1.000 1.000 1.000 -birthday year 0.167 1.846 0.667 0.362 3.800 7.890 0.408 0.083 0.318 0.538 -word clue 0.200 2.028 0.714 0.442 5.819 9.297 0.684 0.157 0.478 0.666 -author creator 0.333 2.539 0.706 0.670 5.691 3.326 0.783 0.241 0.640 0.512 -atom carbon 0.250 2.251 0.800 0.546 4.693 6.270 0.453 0.096 0.487 0.575 -archbishop bishop 0.500 2.944 0.960 0.819 9.162 5.816 0.926 0.404 0.885 0.785 -letter paragraph 0.200 2.028 0.714 0.442 4.263 5.519 0.454 0.089 0.393 0.461 -page paragraph 0.083 1.153 0.250 0.049 0.596 2.612 0.058 0.049 0.087 0.130 -steeple chapel 0.167 1.846 0.706 0.366 3.979 4.386 0.349 0.063 0.327 0.347 -muscle bone 0.250 2.251 0.824 0.548 7.428 8.789 0.799 0.212 0.636 0.703 -muscle tongue 0.250 2.251 0.800 0.546 6.428 9.484 0.629 0.117 0.583 0.735 -boy soldier 0.125 1.558 0.571 0.246 2.334 1.540 0.297 0.083 0.194 0.168 -belly abdomen 1.000 3.638 1.000 0.995 10.217 7.536 1.000 1.000 1.000 1.000 -guy girl 0.200 2.028 0.632 0.449 4.910 4.037 0.568 0.118 0.428 0.381 -bed chair 0.200 2.028 0.800 0.449 6.616 10.855 0.731 0.170 0.523 0.738 -clothes jacket 0.200 2.028 0.762 0.447 6.125 9.840 0.622 0.118 0.440 0.686 -gun knife 0.333 2.539 0.900 0.670 6.763 10.564 0.770 0.198 0.693 0.779 -tin metal 0.500 2.944 0.933 0.818 5.442 13.077 0.615 0.128 0.771 0.949 -bottle container 0.333 2.539 0.875 0.669 5.206 6.699 0.702 0.184 0.615 0.690 -hen turkey 0.250 2.251 0.889 0.549 9.721 14.581 0.893 0.317 0.745 0.896 -meat bread 0.250 2.251 0.769 0.540 6.109 8.084 0.772 0.217 0.566 0.669 -arm bone 0.111 1.440 0.556 0.199 4.402 7.536 0.504 0.103 0.250 0.402 -neck spine 0.333 2.539 0.800 0.635 5.733 10.297 0.521 0.100 0.643 0.833 -apple lemon 0.250 2.251 0.857 0.549 9.614 11.663 0.811 0.183 0.681 0.772 -agony grief 0.143 1.692 0.625 0.296 5.081 11.808 0.479 0.083 0.341 0.699 -assignment task 0.500 2.944 0.941 0.818 6.380 12.635 0.855 0.321 0.801 0.893 -night dawn 0.333 2.539 0.857 0.659 7.269 7.890 0.843 0.270 0.628 0.744 -dinner soup 0.200 2.028 0.750 0.447 6.205 8.301 0.653 0.132 0.500 0.614 -calf bull 0.143 1.692 0.667 0.300 4.946 4.834 0.441 0.074 0.274 0.269 -snow storm 0.200 2.028 0.778 0.449 6.561 10.819 0.642 0.120 0.519 0.737 -nail hand 0.250 2.251 0.800 0.546 6.061 13.887 0.617 0.117 0.563 0.881 -dog horse 0.125 1.558 0.759 0.247 5.414 7.651 0.647 0.145 0.323 0.441 -arm neck 0.200 2.028 0.750 0.447 5.720 12.971 0.600 0.116 0.473 0.819 -ball glove 0.167 1.846 0.737 0.367 5.268 9.016 0.519 0.093 0.393 0.599 -flu fever 0.077 1.073 0.400 0.086 3.140 6.399 0.255 0.052 0.144 0.258 -fee salary 0.143 1.692 0.700 0.301 5.973 10.679 0.725 0.181 0.387 0.644 -nerve brain 0.111 1.440 0.556 0.199 4.402 7.536 0.416 0.075 0.250 0.402 -beast animal 1.000 3.638 1.000 0.999 4.946 4.834 1.000 1.000 1.000 1.000 -dinner chicken 0.091 1.240 0.353 0.092 3.337 5.761 0.332 0.069 0.161 0.247 -girl maid 0.500 2.944 0.952 0.819 7.544 9.297 0.828 0.242 0.843 0.888 -child boy 0.333 2.539 0.909 0.670 6.838 8.082 0.934 0.508 0.697 0.752 -alcohol wine 0.500 2.944 0.947 0.819 7.585 8.865 0.930 0.466 0.845 0.878 -nose mouth 0.250 2.251 0.800 0.546 6.676 13.077 0.669 0.132 0.597 0.860 -street car 0.111 1.440 0.556 0.199 2.306 2.729 0.284 0.079 0.173 0.187 -bell door 0.200 2.028 0.714 0.442 2.306 3.724 0.234 0.062 0.295 0.315 -box hat 0.125 1.558 0.588 0.243 2.332 4.982 0.254 0.069 0.193 0.216 -belief impression 1.000 3.638 1.000 0.999 7.374 12.501 1.000 1.000 1.000 1.000 -bias opinion 0.125 1.558 0.533 0.233 2.645 5.359 0.299 0.075 0.205 0.321 -attention awareness 0.167 1.846 0.667 0.362 4.385 9.003 0.578 0.135 0.347 0.599 -anger mood 0.250 2.251 0.769 0.540 5.073 11.808 0.559 0.111 0.508 0.823 -elegance style 0.500 2.944 0.909 0.805 8.343 9.379 0.935 0.463 0.865 0.730 -beauty age 0.167 1.846 0.545 0.307 2.298 5.951 0.282 0.079 0.250 0.430 -book theme 0.200 2.028 0.714 0.442 5.162 6.513 0.686 0.175 0.442 0.517 -friend mother 0.167 1.846 0.632 0.367 6.263 6.672 0.713 0.166 0.403 0.425 -vitamin iron 0.111 1.440 0.429 0.168 3.337 5.761 0.370 0.081 0.208 0.311 -car factory 0.125 1.558 0.632 0.245 3.979 4.386 0.440 0.090 0.258 0.275 -pact condition 0.200 2.028 0.714 0.442 4.934 7.423 0.575 0.120 0.429 0.567 -chapter choice 0.143 1.692 0.571 0.285 2.036 3.923 0.244 0.073 0.208 0.286 -arithmetic rhythm 0.091 1.240 0.500 0.133 3.292 5.771 0.282 0.056 0.173 0.266 -winner presence 0.100 1.335 0.353 0.092 1.290 0.955 0.152 0.065 0.108 0.101 -belief flower 0.111 1.440 0.333 0.108 0.596 2.612 0.083 0.071 0.125 0.183 -winner goal 0.100 1.335 0.471 0.157 1.532 1.227 0.180 0.069 0.135 0.127 -trick size 0.100 1.335 0.308 0.089 0.802 2.612 0.089 0.060 0.113 0.166 -choice vein 0.111 1.440 0.333 0.108 0.596 2.612 0.066 0.056 0.125 0.183 -hymn conquest 0.077 1.073 0.250 0.049 0.596 2.612 0.056 0.048 0.087 0.130 -endurance band 0.125 1.558 0.375 0.132 2.298 5.951 0.235 0.064 0.143 0.274 -flower endurance 0.111 1.440 0.333 0.108 0.596 2.612 0.064 0.055 0.125 0.183 -hole agreement 0.143 1.692 0.571 0.285 3.140 6.399 0.349 0.079 0.251 0.410 -doctor temper 0.083 1.153 0.267 0.060 0.596 2.612 0.062 0.053 0.094 0.140 -fraternity door 0.083 1.153 0.235 0.040 0.596 2.612 0.058 0.049 0.083 0.121 -task woman 0.091 1.240 0.286 0.073 0.596 2.612 0.081 0.074 0.103 0.152 -fraternity baseball 0.067 0.930 0.222 0.033 0.596 2.612 0.056 0.048 0.075 0.113 -cent size 0.100 1.335 0.308 0.089 0.596 2.612 0.074 0.063 0.113 0.166 -presence door 0.125 1.558 0.632 0.245 4.004 5.984 0.425 0.084 0.259 0.352 -mouse management 0.091 1.240 0.222 0.033 0.596 2.612 0.065 0.055 0.091 0.113 -liquor century 0.091 1.240 0.286 0.073 0.596 2.612 0.072 0.061 0.103 0.152 -task straw 0.077 1.073 0.250 0.049 0.596 2.612 0.069 0.058 0.087 0.130 -night chapter 0.250 2.251 0.769 0.540 3.800 7.890 0.423 0.088 0.438 0.660 -pollution president 0.091 1.240 0.500 0.133 2.332 4.982 0.224 0.058 0.144 0.233 -gun trick 0.125 1.558 0.588 0.246 2.334 4.982 0.260 0.070 0.194 0.303 -bath trick 0.143 1.692 0.667 0.300 3.734 7.001 0.333 0.064 0.252 0.363 -diet apple 0.100 1.335 0.400 0.138 3.337 5.761 0.307 0.062 0.190 0.287 -chapter tail 0.125 1.558 0.462 0.206 2.691 7.001 0.257 0.060 0.207 0.405 -course stomach 0.111 1.440 0.333 0.108 0.802 2.612 0.088 0.057 0.130 0.152 -hymn straw 0.077 1.073 0.250 0.049 0.596 2.612 0.053 0.045 0.087 0.130 -dentist colonel 0.077 1.073 0.462 0.091 2.334 1.540 0.215 0.055 0.123 0.105 -wife straw 0.091 1.240 0.400 0.086 1.532 1.227 0.152 0.055 0.107 0.110 -hole wife 0.100 1.335 0.471 0.157 1.532 1.227 0.187 0.070 0.135 0.127 -pupil president 0.167 1.846 0.632 0.367 2.334 2.612 0.316 0.090 0.252 0.220 -bath wife 0.083 1.153 0.421 0.105 1.532 1.227 0.162 0.059 0.113 0.107 -people cent 0.111 1.440 0.333 0.108 0.596 2.612 0.083 0.071 0.125 0.183 -formula log 0.111 1.440 0.429 0.168 3.337 5.761 0.312 0.064 0.183 0.266 -woman fur 0.091 1.240 0.444 0.128 1.532 2.612 0.172 0.064 0.123 0.130 -apple sunshine 0.091 1.240 0.235 0.040 0.802 0.929 0.080 0.051 0.091 0.091 -gun dawn 0.100 1.335 0.471 0.157 2.036 3.923 0.222 0.065 0.149 0.211 -meal waist 0.111 1.440 0.333 0.108 0.802 0.929 0.080 0.051 0.130 0.133 -camera president 0.083 1.153 0.421 0.105 1.532 1.227 0.172 0.064 0.113 0.107 -liquor band 0.100 1.335 0.308 0.089 0.802 2.612 0.092 0.065 0.113 0.166 -stomach vein 0.200 2.028 0.714 0.442 4.402 7.536 0.419 0.076 0.400 0.573 -gun fur 0.111 1.440 0.556 0.199 2.306 2.729 0.239 0.064 0.173 0.187 -couch baseball 0.111 1.440 0.600 0.201 3.258 3.625 0.311 0.065 0.205 0.219 -worker camera 0.100 1.335 0.471 0.157 1.532 1.227 0.202 0.076 0.135 0.127 -deck mouse 0.125 1.558 0.632 0.245 3.258 3.625 0.289 0.059 0.228 0.243 -rice boy 0.143 1.692 0.600 0.301 2.334 1.540 0.249 0.066 0.219 0.190 -people gun 0.100 1.335 0.308 0.089 0.596 2.612 0.081 0.071 0.113 0.166 -cliff tail 0.125 1.558 0.429 0.168 1.290 0.955 0.131 0.062 0.146 0.150 -ankle window 0.077 1.073 0.250 0.049 0.802 0.929 0.078 0.050 0.091 0.093 -princess island 0.111 1.440 0.429 0.168 1.290 0.955 0.139 0.059 0.143 0.134 -container mouse 0.200 2.028 0.750 0.447 3.258 3.625 0.388 0.089 0.341 0.360 -wagon container 0.333 2.539 0.875 0.669 5.206 6.699 0.676 0.167 0.615 0.690 -dollar people 0.143 1.692 0.400 0.162 0.596 2.612 0.081 0.069 0.160 0.230 -bath balloon 0.143 1.692 0.625 0.296 3.258 3.625 0.291 0.059 0.218 0.235 -stomach bedroom 0.083 1.153 0.267 0.060 0.802 0.929 0.081 0.052 0.098 0.101 -bicycle bedroom 0.111 1.440 0.556 0.199 2.306 2.729 0.244 0.065 0.173 0.187 -log bath 0.125 1.558 0.632 0.245 3.258 3.625 0.298 0.061 0.228 0.243 -bowl tail 0.167 1.846 0.667 0.362 6.486 11.131 0.581 0.097 0.460 0.706 diff --git a/sematch/eval/word_similarity/results_original/graph_ws353-sim.txt b/sematch/eval/word_similarity/results_original/graph_ws353-sim.txt deleted file mode 100644 index 2c4f5f7..0000000 --- a/sematch/eval/word_similarity/results_original/graph_ws353-sim.txt +++ /dev/null @@ -1,190 +0,0 @@ -word1 word2 path lch wup li res res_graph lin jcn wpath wpath_graph -tiger cat 0.500 2.944 0.966 0.819 9.841 12.278 0.894 0.301 0.900 0.939 -tiger tiger 1.000 3.638 1.000 1.000 12.268 10.819 1.000 1.000 1.000 1.000 -plane car 0.143 1.692 0.727 0.301 5.922 6.108 0.719 0.178 0.385 0.394 -train car 0.167 1.846 0.737 0.367 5.769 6.095 0.679 0.155 0.420 0.438 -television radio 0.333 2.539 0.909 0.670 9.123 15.274 0.920 0.386 0.793 0.938 -media radio 0.250 2.251 0.824 0.548 7.021 3.763 0.832 0.261 0.615 0.436 -bread butter 0.250 2.251 0.750 0.546 6.880 8.826 0.711 0.152 0.607 0.705 -cucumber potato 0.250 2.251 0.824 0.548 9.442 9.951 0.659 0.103 0.686 0.754 -doctor nurse 0.250 2.251 0.870 0.549 7.930 5.858 0.835 0.242 0.662 0.552 -professor doctor 0.143 1.692 0.545 0.301 6.394 4.280 0.704 0.157 0.410 0.302 -student professor 0.125 1.558 0.571 0.246 2.334 1.540 0.276 0.076 0.194 0.168 -wood forest 1.000 3.638 1.000 0.995 9.002 10.774 1.000 1.000 1.000 1.000 -money cash 0.333 2.539 0.875 0.669 6.445 9.721 0.789 0.230 0.678 0.814 -king queen 1.000 3.638 1.000 1.000 9.568 14.581 1.000 1.000 1.000 1.000 -king rook 0.333 2.539 0.909 0.670 8.505 14.581 0.772 0.166 0.769 0.928 -bishop rabbi 0.200 2.028 0.545 0.449 6.962 5.572 0.666 0.125 0.542 0.455 -fuck sex 0.333 2.539 0.857 0.667 6.690 13.664 0.770 0.200 0.690 0.913 -football soccer 0.500 2.944 0.880 0.819 11.064 14.175 0.906 0.303 0.922 0.959 -football basketball 0.333 2.539 0.900 0.670 8.716 15.274 0.754 0.149 0.771 0.938 -football tennis 0.167 1.846 0.783 0.368 8.716 11.285 0.770 0.161 0.583 0.713 -Arafat Jackson 0.250 2.251 0.571 0.549 6.737 7.455 0.000 0.000 0.600 0.638 -physics chemistry 0.333 2.539 0.900 0.670 8.716 8.082 0.818 0.205 0.778 0.752 -vodka gin 0.333 2.539 0.909 0.670 8.856 12.229 0.000 0.000 0.783 0.884 -vodka brandy 0.333 2.539 0.909 0.670 8.856 12.229 0.000 0.000 0.783 0.884 -car automobile 1.000 3.638 1.000 1.000 7.591 7.386 1.000 1.000 1.000 1.000 -gem jewel 1.000 3.638 1.000 1.000 12.068 12.876 1.000 1.000 1.000 1.000 -journey voyage 0.500 2.944 0.952 0.819 6.826 9.326 0.778 0.204 0.821 0.889 -boy lad 0.500 2.944 0.947 0.819 8.399 10.796 0.831 0.226 0.867 0.918 -coast shore 0.500 2.944 0.909 0.805 9.416 11.560 0.963 0.582 0.891 0.930 -asylum madhouse 0.500 2.944 0.952 0.819 9.475 10.520 0.856 0.238 0.892 0.913 -magician wizard 1.000 3.638 0.667 1.000 11.981 14.175 1.000 1.000 1.000 1.000 -furnace stove 0.100 1.335 0.526 0.163 2.306 2.729 0.228 0.060 0.157 0.170 -food fruit 0.100 1.335 0.400 0.138 1.593 3.724 0.161 0.084 0.137 0.203 -bird cock 0.500 2.944 0.952 0.819 7.678 9.339 0.774 0.182 0.847 0.889 -bird crane 0.250 2.251 0.870 0.549 7.678 9.339 0.748 0.162 0.649 0.728 -food rooster 0.063 0.865 0.211 0.027 0.802 0.929 0.092 0.059 0.074 0.076 -money dollar 0.200 2.028 0.778 0.449 6.445 9.721 0.722 0.172 0.513 0.686 -money currency 0.500 2.944 0.933 0.818 6.445 9.721 0.864 0.336 0.808 0.897 -tiger jaguar 0.333 2.539 0.933 0.670 9.841 12.278 0.797 0.167 0.818 0.886 -tiger feline 0.333 2.539 0.929 0.670 8.663 10.867 0.832 0.222 0.776 0.850 -tiger carnivore 0.250 2.251 0.889 0.549 7.912 9.490 0.788 0.190 0.661 0.735 -tiger mammal 0.167 1.846 0.800 0.368 5.406 7.040 0.615 0.129 0.401 0.490 -tiger animal 0.250 2.251 0.800 0.546 4.946 4.834 0.578 0.122 0.354 0.317 -tiger organism 0.333 2.539 0.857 0.667 2.224 1.480 0.309 0.091 0.451 0.410 -tiger fauna 0.250 2.251 0.800 0.546 4.946 4.834 0.578 0.122 0.354 0.317 -psychology psychiatry 0.143 1.692 0.727 0.301 7.187 8.052 0.655 0.117 0.453 0.501 -psychology science 0.500 2.944 0.941 0.818 7.187 8.052 0.847 0.279 0.833 0.858 -psychology discipline 0.333 2.539 0.875 0.669 5.589 7.972 0.728 0.193 0.635 0.748 -planet star 0.333 2.539 0.857 0.667 6.932 8.564 0.874 0.334 0.701 0.772 -planet moon 0.250 2.251 0.800 0.546 6.932 8.564 0.781 0.204 0.610 0.693 -planet sun 0.250 2.251 0.800 0.546 6.932 8.564 0.810 0.236 0.610 0.693 -precedent example 0.500 2.944 0.923 0.815 8.077 14.581 0.817 0.217 0.858 0.963 -precedent antecedent 0.167 1.846 0.615 0.348 3.848 6.513 0.305 0.054 0.321 0.461 -cup tableware 0.333 2.539 0.889 0.670 7.490 8.640 0.840 0.260 0.727 0.775 -cup artifact 0.250 2.251 0.769 0.540 2.306 2.729 0.365 0.111 0.358 0.380 -cup object 0.167 1.846 0.545 0.307 2.620 4.436 0.261 0.100 0.211 0.212 -jaguar cat 0.500 2.944 0.966 0.819 9.841 12.278 0.880 0.272 0.900 0.939 -jaguar car 0.059 0.804 0.333 0.039 1.532 1.227 0.152 0.055 0.081 0.076 -mile kilometer 0.250 2.251 0.800 0.546 6.061 13.887 0.579 0.102 0.563 0.881 -skin eye 0.167 1.846 0.667 0.362 4.402 7.536 0.490 0.098 0.276 0.434 -Japanese American 0.250 2.251 0.632 0.549 6.424 12.138 0.695 0.156 0.565 0.730 -century year 0.333 2.539 0.833 0.659 3.800 7.890 0.484 0.110 0.539 0.744 -announcement news 0.200 2.028 0.667 0.425 3.848 6.513 0.490 0.111 0.371 0.517 -doctor personnel 0.083 1.153 0.267 0.060 0.596 2.612 0.076 0.066 0.094 0.140 -Harvard Yale 0.333 2.539 0.917 0.670 9.922 7.370 0.860 0.237 0.821 0.721 -hospital infrastructure 0.083 1.153 0.267 0.060 0.596 2.612 0.061 0.052 0.094 0.140 -life death 0.333 2.539 0.857 0.667 7.411 7.001 0.888 0.349 0.723 0.582 -travel activity 0.167 1.846 0.667 0.362 2.332 4.982 0.500 0.177 0.252 0.378 -type kind 0.500 2.944 0.947 0.819 6.421 6.450 0.896 0.402 0.807 0.808 -street place 0.167 1.846 0.615 0.348 3.140 6.399 0.366 0.084 0.287 0.455 -street avenue 0.500 2.944 0.947 0.819 8.670 8.139 0.913 0.377 0.874 0.860 -street block 0.167 1.846 0.667 0.362 2.855 5.951 0.306 0.087 0.251 0.350 -cell phone 0.333 2.539 0.900 0.670 8.723 10.943 0.959 0.575 0.778 0.842 -dividend payment 0.333 2.539 0.889 0.670 6.737 13.664 0.726 0.164 0.692 0.913 -calculation computation 1.000 3.638 1.000 1.000 7.760 10.956 1.000 1.000 1.000 1.000 -profit loss 0.167 1.846 0.706 0.366 6.161 13.887 0.770 0.213 0.442 0.816 -dollar yen 0.250 2.251 0.800 0.546 6.875 7.001 0.610 0.102 0.607 0.284 -dollar buck 1.000 3.638 1.000 1.000 9.284 9.721 1.000 1.000 1.000 1.000 -phone equipment 0.333 2.539 0.875 0.669 5.268 9.016 0.753 0.224 0.618 0.789 -liquid water 0.500 2.944 0.933 0.818 8.115 14.581 0.994 0.916 0.859 0.963 -marathon sprint 0.091 1.240 0.500 0.133 2.332 4.982 0.190 0.048 0.144 0.233 -seafood food 0.500 2.944 0.909 0.805 6.109 8.084 0.790 0.235 0.796 0.859 -seafood lobster 0.333 2.539 0.857 0.667 9.360 10.720 0.809 0.185 0.801 0.845 -lobster food 0.250 2.251 0.769 0.540 6.109 8.084 0.615 0.115 0.566 0.669 -lobster wine 0.091 1.240 0.375 0.113 3.337 5.761 0.297 0.059 0.174 0.266 -championship tournament 0.333 2.539 0.857 0.667 6.609 5.809 0.633 0.115 0.686 0.646 -man woman 0.333 2.539 0.706 0.670 4.910 4.037 0.787 0.273 0.599 0.552 -man governor 0.200 2.028 0.667 0.449 3.258 3.625 0.402 0.093 0.296 0.261 -murder manslaughter 0.333 2.539 0.917 0.670 8.560 9.849 0.785 0.176 0.772 0.818 -opera performance 0.100 1.335 0.308 0.089 0.802 2.612 0.084 0.057 0.117 0.152 -Mexico Brazil 0.200 2.028 0.800 0.449 6.134 7.312 0.495 0.074 0.496 0.561 -glass metal 0.167 1.846 0.545 0.307 3.337 5.761 0.454 0.111 0.296 0.420 -aluminum metal 0.500 2.944 0.933 0.818 5.442 13.077 0.667 0.155 0.771 0.949 -rock jazz 0.333 2.539 0.875 0.669 8.409 9.683 0.864 0.275 0.766 0.813 -museum theater 0.143 1.692 0.625 0.296 2.306 2.729 0.248 0.067 0.218 0.235 -shower thunderstorm 0.143 1.692 0.700 0.301 6.561 10.819 0.000 0.000 0.419 0.651 -monk oracle 0.125 1.558 0.571 0.246 2.334 1.540 0.226 0.059 0.194 0.168 -cup food 0.167 1.846 0.667 0.362 5.279 7.383 0.672 0.163 0.394 0.510 -journal association 0.143 1.692 0.400 0.162 0.802 2.612 0.081 0.059 0.160 0.230 -street children 0.100 1.335 0.471 0.157 1.532 1.227 0.198 0.074 0.135 0.127 -car flight 0.125 1.558 0.588 0.243 2.306 2.729 0.256 0.070 0.193 0.208 -space chemistry 0.167 1.846 0.444 0.198 0.802 2.612 0.089 0.057 0.186 0.264 -situation conclusion 0.143 1.692 0.625 0.296 2.332 4.982 0.346 0.102 0.219 0.336 -word similarity 0.111 1.440 0.455 0.108 3.292 5.771 0.335 0.071 0.148 0.232 -peace plan 0.100 1.335 0.308 0.089 0.596 2.612 0.076 0.065 0.113 0.166 -consumer energy 0.100 1.335 0.308 0.089 0.802 0.929 0.114 0.074 0.117 0.120 -ministry culture 0.125 1.558 0.533 0.233 3.336 4.982 0.314 0.064 0.231 0.235 -investigation effort 0.250 2.251 0.824 0.548 4.806 8.647 0.693 0.190 0.493 0.697 -image surface 0.200 2.028 0.714 0.442 3.292 5.771 0.378 0.099 0.295 0.398 -life term 0.333 2.539 0.857 0.667 6.494 14.581 0.905 0.423 0.680 0.928 -start match 0.143 1.692 0.571 0.285 2.691 7.001 0.307 0.078 0.233 0.443 -board recommendation 0.083 1.153 0.267 0.060 0.596 2.612 0.068 0.057 0.094 0.140 -lad brother 0.200 2.028 0.667 0.449 2.334 1.540 0.255 0.068 0.296 0.261 -observation architecture 0.167 1.846 0.706 0.366 3.144 5.519 0.295 0.067 0.287 0.407 -coast hill 0.200 2.028 0.667 0.425 5.885 5.283 0.599 0.113 0.482 0.448 -deployment departure 0.200 2.028 0.714 0.442 2.332 4.982 0.212 0.054 0.296 0.432 -benchmark index 0.250 2.251 0.769 0.540 6.110 8.579 0.514 0.080 0.566 0.693 -attempt peace 0.100 1.335 0.308 0.089 0.596 2.612 0.077 0.065 0.113 0.166 -start year 0.167 1.846 0.545 0.307 2.691 7.001 0.385 0.104 0.267 0.488 -focus life 0.143 1.692 0.533 0.251 3.140 6.399 0.371 0.086 0.224 0.373 -development issue 0.250 2.251 0.750 0.458 4.089 9.717 0.567 0.138 0.454 0.745 -theater history 0.143 1.692 0.500 0.251 1.593 3.724 0.182 0.065 0.192 0.277 -situation isolation 0.250 2.251 0.727 0.520 3.140 6.399 0.403 0.097 0.402 0.582 -profit warning 0.091 1.240 0.286 0.073 0.596 2.612 0.074 0.063 0.103 0.152 -media trading 0.143 1.692 0.625 0.296 2.332 4.982 0.231 0.060 0.219 0.336 -chance credibility 0.167 1.846 0.500 0.251 2.298 5.951 0.268 0.074 0.218 0.386 -precedent information 0.333 2.539 0.833 0.659 5.081 9.032 0.606 0.131 0.608 0.790 -architecture century 0.100 1.335 0.308 0.089 0.596 2.612 0.063 0.054 0.113 0.166 -population development 0.143 1.692 0.727 0.301 5.654 6.232 0.642 0.137 0.370 0.401 -peace atmosphere 0.200 2.028 0.714 0.442 4.235 6.780 0.501 0.106 0.391 0.532 -morality marriage 0.167 1.846 0.545 0.307 2.298 5.951 0.306 0.088 0.250 0.430 -minority peace 0.167 1.846 0.615 0.348 3.140 6.399 0.303 0.065 0.287 0.455 -atmosphere landscape 0.111 1.440 0.500 0.191 3.012 2.612 0.299 0.066 0.197 0.176 -report gain 0.125 1.558 0.364 0.132 0.596 2.612 0.084 0.071 0.140 0.204 -music project 0.250 2.251 0.800 0.546 3.144 5.519 0.432 0.108 0.402 0.533 -seven series 0.111 1.440 0.375 0.113 3.037 5.511 0.316 0.071 0.165 0.255 -experience music 0.167 1.846 0.615 0.348 2.645 5.359 0.329 0.086 0.240 0.324 -school center 0.333 2.539 0.875 0.669 5.715 5.359 0.669 0.150 0.642 0.591 -five month 0.125 1.558 0.462 0.206 2.691 7.001 0.374 0.100 0.207 0.405 -announcement production 0.143 1.692 0.500 0.251 2.620 4.436 0.301 0.076 0.230 0.310 -morality importance 0.200 2.028 0.667 0.425 3.965 9.379 0.583 0.150 0.377 0.670 -money operation 0.111 1.440 0.333 0.108 0.596 2.612 0.096 0.082 0.125 0.183 -delay news 0.125 1.558 0.533 0.233 2.036 3.923 0.213 0.062 0.184 0.255 -practice institution 0.333 2.539 0.833 0.659 7.355 13.077 0.871 0.315 0.721 0.902 -century nation 0.125 1.558 0.364 0.132 0.596 2.612 0.083 0.070 0.140 0.204 -coast forest 0.167 1.846 0.545 0.307 1.290 2.612 0.131 0.055 0.211 0.198 -shore woodland 0.200 2.028 0.600 0.375 1.290 0.955 0.136 0.057 0.250 0.236 -drink car 0.091 1.240 0.286 0.073 0.802 0.929 0.112 0.073 0.107 0.110 -president medal 0.077 1.073 0.235 0.040 0.596 2.612 0.063 0.054 0.081 0.121 -prejudice recognition 0.167 1.846 0.667 0.362 6.039 10.659 0.555 0.094 0.435 0.683 -viewer serial 0.091 1.240 0.500 0.133 2.306 2.729 0.212 0.061 0.143 0.155 -peace insurance 0.250 2.251 0.824 0.548 8.459 13.887 0.842 0.256 0.688 0.881 -Mars water 0.111 1.440 0.500 0.191 1.532 2.612 0.155 0.066 0.150 0.166 -media gain 0.143 1.692 0.500 0.251 3.037 5.951 0.369 0.088 0.220 0.386 -precedent cognition 0.250 2.251 0.727 0.520 2.645 5.359 0.369 0.100 0.376 0.524 -announcement effort 0.100 1.335 0.308 0.089 0.596 2.612 0.085 0.072 0.113 0.166 -line insurance 0.200 2.028 0.714 0.442 5.176 10.997 0.559 0.121 0.442 0.744 -crane implement 0.200 2.028 0.750 0.447 3.258 3.625 0.359 0.079 0.341 0.360 -drink mother 0.167 1.846 0.615 0.348 4.615 6.858 0.595 0.137 0.359 0.480 -opera industry 0.091 1.240 0.286 0.073 0.596 2.612 0.065 0.055 0.103 0.152 -listing proximity 0.077 1.073 0.316 0.062 1.593 3.724 0.135 0.057 0.099 0.150 -precedent collection 0.333 2.539 0.800 0.635 5.053 7.853 0.608 0.133 0.607 0.743 -cup article 0.200 2.028 0.750 0.447 7.332 8.640 0.830 0.250 0.562 0.632 -sign recess 0.200 2.028 0.750 0.447 3.979 4.386 0.438 0.089 0.378 0.399 -reason hypertension 0.111 1.440 0.556 0.199 4.235 6.780 0.385 0.069 0.243 0.362 -direction combination 0.143 1.692 0.667 0.300 3.144 5.519 0.334 0.088 0.252 0.363 -Wednesday news 0.083 1.153 0.267 0.060 0.596 2.612 0.067 0.056 0.094 0.140 -glass magician 0.125 1.558 0.471 0.206 2.283 1.532 0.214 0.056 0.192 0.167 -cemetery woodland 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.052 0.143 0.134 -cup substance 0.143 1.692 0.571 0.285 4.615 6.858 0.614 0.147 0.318 0.435 -forest graveyard 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.052 0.143 0.134 -stock egg 0.143 1.692 0.625 0.296 5.279 7.383 0.545 0.102 0.351 0.464 -energy secretary 0.091 1.240 0.286 0.073 0.802 0.929 0.102 0.066 0.107 0.110 -precedent group 0.250 2.251 0.667 0.458 2.855 3.353 0.396 0.103 0.387 0.413 -production hike 0.125 1.558 0.588 0.243 2.332 4.982 0.232 0.064 0.194 0.303 -stock phone 0.143 1.692 0.700 0.301 4.076 6.754 0.414 0.093 0.293 0.429 -holy sex 0.100 1.335 0.308 0.089 0.802 0.929 0.091 0.059 0.117 0.120 -stock CD 0.167 1.846 0.737 0.367 4.076 6.754 0.372 0.068 0.332 0.474 -drink ear 0.125 1.558 0.462 0.206 3.968 4.948 0.395 0.076 0.257 0.301 -delay racism 0.143 1.692 0.625 0.296 2.332 4.982 0.211 0.054 0.219 0.336 -stock life 0.167 1.846 0.615 0.348 3.140 6.399 0.368 0.085 0.224 0.373 -stock jaguar 0.167 1.846 0.815 0.368 5.414 7.651 0.494 0.083 0.401 0.524 -monk slave 0.200 2.028 0.667 0.449 2.334 1.540 0.254 0.068 0.296 0.261 -lad wizard 0.200 2.028 0.667 0.449 2.334 1.540 0.255 0.068 0.296 0.261 -sugar approach 0.091 1.240 0.286 0.073 0.802 2.612 0.087 0.056 0.107 0.140 -noon string 0.083 1.153 0.267 0.060 0.596 2.612 0.066 0.056 0.094 0.140 -chord smile 0.091 1.240 0.375 0.113 2.620 4.436 0.246 0.059 0.152 0.212 -professor cucumber 0.077 1.073 0.500 0.090 2.224 1.480 0.065 0.042 0.120 0.104 -king cabbage 0.100 1.335 0.571 0.164 3.337 5.761 0.305 0.062 0.154 0.218 diff --git a/sematch/eval/word_similarity/results_original/graph_ws353.txt b/sematch/eval/word_similarity/results_original/graph_ws353.txt deleted file mode 100644 index cef445f..0000000 --- a/sematch/eval/word_similarity/results_original/graph_ws353.txt +++ /dev/null @@ -1,322 +0,0 @@ -word1 word2 path lch wup li res res_graph lin jcn wpath wpath_graph -love sex 0.500 2.944 0.923 0.815 6.690 13.664 0.852 0.300 0.817 0.955 -tiger cat 0.500 2.944 0.966 0.819 9.841 12.278 0.894 0.301 0.900 0.939 -tiger tiger 1.000 3.638 1.000 1.000 12.268 10.819 1.000 1.000 1.000 1.000 -book paper 0.333 2.539 0.875 0.669 5.455 5.519 0.674 0.159 0.628 0.558 -computer keyboard 0.250 2.251 0.824 0.548 4.076 6.754 0.374 0.068 0.453 0.601 -computer internet 0.125 1.558 0.632 0.245 3.258 3.625 0.330 0.070 0.228 0.243 -plane car 0.143 1.692 0.727 0.301 5.922 6.108 0.719 0.178 0.385 0.394 -train car 0.167 1.846 0.737 0.367 5.769 6.095 0.679 0.155 0.420 0.438 -television radio 0.333 2.539 0.909 0.670 9.123 15.274 0.920 0.386 0.793 0.938 -media radio 0.250 2.251 0.824 0.548 7.021 3.763 0.832 0.261 0.615 0.436 -bread butter 0.250 2.251 0.750 0.546 6.880 8.826 0.711 0.152 0.607 0.705 -cucumber potato 0.250 2.251 0.824 0.548 9.442 9.951 0.659 0.103 0.686 0.754 -doctor nurse 0.250 2.251 0.870 0.549 7.930 5.858 0.835 0.242 0.662 0.552 -professor doctor 0.143 1.692 0.545 0.301 6.394 4.280 0.704 0.157 0.410 0.302 -student professor 0.125 1.558 0.571 0.246 2.334 1.540 0.276 0.076 0.194 0.168 -company stock 0.167 1.846 0.615 0.348 3.336 6.399 0.483 0.123 0.296 0.343 -stock market 0.143 1.692 0.556 0.251 3.336 3.440 0.455 0.111 0.240 0.260 -stock phone 0.143 1.692 0.700 0.301 4.076 6.754 0.414 0.093 0.293 0.429 -stock CD 0.167 1.846 0.737 0.367 4.076 6.754 0.372 0.068 0.332 0.474 -stock jaguar 0.167 1.846 0.815 0.368 5.414 7.651 0.494 0.083 0.401 0.524 -stock egg 0.143 1.692 0.625 0.296 5.279 7.383 0.545 0.102 0.351 0.464 -stock life 0.167 1.846 0.615 0.348 3.140 6.399 0.368 0.085 0.224 0.373 -book library 0.333 2.539 0.800 0.635 5.053 7.853 0.537 0.103 0.607 0.743 -bank money 0.143 1.692 0.571 0.285 4.470 8.737 0.483 0.095 0.311 0.539 -wood forest 1.000 3.638 1.000 0.995 9.002 10.774 1.000 1.000 1.000 1.000 -money cash 0.333 2.539 0.875 0.669 6.445 9.721 0.789 0.230 0.678 0.814 -professor cucumber 0.077 1.073 0.500 0.090 2.224 1.480 0.065 0.042 0.120 0.104 -king cabbage 0.100 1.335 0.571 0.164 3.337 5.761 0.305 0.062 0.154 0.218 -king queen 1.000 3.638 1.000 1.000 9.568 14.581 1.000 1.000 1.000 1.000 -king rook 0.333 2.539 0.909 0.670 8.505 14.581 0.772 0.166 0.769 0.928 -bishop rabbi 0.200 2.028 0.545 0.449 6.962 5.572 0.666 0.125 0.542 0.455 -Jerusalem Israel 0.143 1.692 0.700 0.301 4.389 2.759 0.367 0.062 0.307 0.236 -Jerusalem Palestinian 0.063 0.865 0.286 0.042 1.290 0.955 0.000 0.000 0.082 0.076 -holy sex 0.100 1.335 0.308 0.089 0.802 0.929 0.091 0.059 0.117 0.120 -fuck sex 0.333 2.539 0.857 0.667 6.690 13.664 0.770 0.200 0.690 0.913 -football soccer 0.500 2.944 0.880 0.819 11.064 14.175 0.906 0.303 0.922 0.959 -football basketball 0.333 2.539 0.900 0.670 8.716 15.274 0.754 0.149 0.771 0.938 -football tennis 0.167 1.846 0.783 0.368 8.716 11.285 0.770 0.161 0.583 0.713 -tennis racket 0.111 1.440 0.600 0.201 3.144 5.519 0.262 0.054 0.201 0.300 -Arafat terror 0.167 1.846 0.632 0.367 2.334 1.540 0.000 0.000 0.252 0.220 -Arafat Jackson 0.250 2.251 0.571 0.549 6.737 7.455 0.000 0.000 0.600 0.638 -movie star 0.125 1.558 0.533 0.233 1.593 3.724 0.189 0.071 0.167 0.173 -movie popcorn 0.071 0.999 0.364 0.058 1.532 1.227 0.000 0.000 0.091 0.086 -movie critic 0.091 1.240 0.444 0.128 1.532 1.227 0.180 0.067 0.123 0.116 -movie theater 0.143 1.692 0.625 0.296 2.306 3.923 0.293 0.083 0.218 0.286 -physics proton 0.067 0.930 0.222 0.033 0.802 0.929 0.066 0.042 0.079 0.081 -physics chemistry 0.333 2.539 0.900 0.670 8.716 8.082 0.818 0.205 0.778 0.752 -space chemistry 0.167 1.846 0.444 0.198 0.802 2.612 0.089 0.057 0.186 0.264 -alcohol chemistry 0.200 2.028 0.571 0.442 3.886 6.149 0.399 0.079 0.373 0.496 -vodka gin 0.333 2.539 0.909 0.670 8.856 12.229 0.000 0.000 0.783 0.884 -vodka brandy 0.333 2.539 0.909 0.670 8.856 12.229 0.000 0.000 0.783 0.884 -drink car 0.091 1.240 0.286 0.073 0.802 0.929 0.112 0.073 0.107 0.110 -drink ear 0.125 1.558 0.462 0.206 3.968 4.948 0.395 0.076 0.257 0.301 -drink mouth 0.143 1.692 0.500 0.168 3.968 4.982 0.426 0.086 0.233 0.274 -baby mother 0.200 2.028 0.632 0.449 6.257 7.095 0.684 0.147 0.502 0.481 -drink mother 0.167 1.846 0.615 0.348 4.615 6.858 0.595 0.137 0.359 0.480 -car automobile 1.000 3.638 1.000 1.000 7.591 7.386 1.000 1.000 1.000 1.000 -gem jewel 1.000 3.638 1.000 1.000 12.068 12.876 1.000 1.000 1.000 1.000 -journey voyage 0.500 2.944 0.952 0.819 6.826 9.326 0.778 0.204 0.821 0.889 -boy lad 0.500 2.944 0.947 0.819 8.399 10.796 0.831 0.226 0.867 0.918 -coast shore 0.500 2.944 0.909 0.805 9.416 11.560 0.963 0.582 0.891 0.930 -asylum madhouse 0.500 2.944 0.952 0.819 9.475 10.520 0.856 0.238 0.892 0.913 -magician wizard 1.000 3.638 0.667 1.000 11.981 14.175 1.000 1.000 1.000 1.000 -furnace stove 0.100 1.335 0.526 0.163 2.306 2.729 0.228 0.060 0.157 0.170 -food fruit 0.100 1.335 0.400 0.138 1.593 3.724 0.161 0.084 0.137 0.203 -bird cock 0.500 2.944 0.952 0.819 7.678 9.339 0.774 0.182 0.847 0.889 -bird crane 0.250 2.251 0.870 0.549 7.678 9.339 0.748 0.162 0.649 0.728 -tool implement 0.500 2.944 0.933 0.818 5.877 10.564 0.947 0.604 0.788 0.914 -brother monk 0.500 2.944 0.952 0.819 9.262 8.392 0.986 0.797 0.888 0.867 -crane implement 0.200 2.028 0.750 0.447 3.258 3.625 0.359 0.079 0.341 0.360 -lad brother 0.200 2.028 0.667 0.449 2.334 1.540 0.255 0.068 0.296 0.261 -monk oracle 0.125 1.558 0.571 0.246 2.334 1.540 0.226 0.059 0.194 0.168 -cemetery woodland 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.052 0.143 0.134 -food rooster 0.063 0.865 0.211 0.027 0.802 0.929 0.092 0.059 0.074 0.076 -coast hill 0.200 2.028 0.667 0.425 5.885 5.283 0.599 0.113 0.482 0.448 -forest graveyard 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.052 0.143 0.134 -shore woodland 0.200 2.028 0.600 0.375 1.290 0.955 0.136 0.057 0.250 0.236 -monk slave 0.200 2.028 0.667 0.449 2.334 1.540 0.254 0.068 0.296 0.261 -coast forest 0.167 1.846 0.545 0.307 1.290 2.612 0.131 0.055 0.211 0.198 -lad wizard 0.200 2.028 0.667 0.449 2.334 1.540 0.255 0.068 0.296 0.261 -chord smile 0.091 1.240 0.375 0.113 2.620 4.436 0.246 0.059 0.152 0.212 -glass magician 0.125 1.558 0.471 0.206 2.283 1.532 0.214 0.056 0.192 0.167 -noon string 0.083 1.153 0.267 0.060 0.596 2.612 0.066 0.056 0.094 0.140 -money dollar 0.200 2.028 0.778 0.449 6.445 9.721 0.722 0.172 0.513 0.686 -money cash 0.333 2.539 0.875 0.669 6.445 9.721 0.789 0.230 0.678 0.814 -money currency 0.500 2.944 0.933 0.818 6.445 9.721 0.864 0.336 0.808 0.897 -money wealth 0.500 2.944 0.923 0.815 8.660 8.737 0.996 0.936 0.874 0.539 -money property 0.333 2.539 0.833 0.659 6.897 9.117 0.883 0.353 0.700 0.793 -money possession 0.250 2.251 0.727 0.520 4.470 8.737 0.677 0.190 0.475 0.701 -money bank 0.143 1.692 0.571 0.285 4.470 8.737 0.483 0.095 0.311 0.539 -money deposit 0.333 2.539 0.875 0.669 8.042 12.229 0.812 0.212 0.751 0.884 -money withdrawal 0.091 1.240 0.286 0.073 0.596 2.612 0.067 0.057 0.103 0.152 -money laundering 0.077 1.073 0.250 0.049 0.596 2.612 0.058 0.049 0.087 0.130 -money operation 0.111 1.440 0.333 0.108 0.596 2.612 0.096 0.082 0.125 0.183 -tiger jaguar 0.333 2.539 0.933 0.670 9.841 12.278 0.797 0.167 0.818 0.886 -tiger feline 0.333 2.539 0.929 0.670 8.663 10.867 0.832 0.222 0.776 0.850 -tiger carnivore 0.250 2.251 0.889 0.549 7.912 9.490 0.788 0.190 0.661 0.735 -tiger mammal 0.167 1.846 0.800 0.368 5.406 7.040 0.615 0.129 0.401 0.490 -tiger animal 0.250 2.251 0.800 0.546 4.946 4.834 0.578 0.122 0.354 0.317 -tiger organism 0.333 2.539 0.857 0.667 2.224 1.480 0.309 0.091 0.451 0.410 -tiger fauna 0.250 2.251 0.800 0.546 4.946 4.834 0.578 0.122 0.354 0.317 -tiger zoo 0.125 1.558 0.533 0.233 1.532 1.227 0.126 0.045 0.167 0.158 -psychology psychiatry 0.143 1.692 0.727 0.301 7.187 8.052 0.655 0.117 0.453 0.501 -psychology anxiety 0.077 1.073 0.250 0.049 0.596 2.612 0.068 0.057 0.087 0.130 -psychology fear 0.077 1.073 0.250 0.049 0.596 2.612 0.066 0.056 0.087 0.130 -psychology depression 0.083 1.153 0.353 0.092 1.593 3.724 0.153 0.058 0.115 0.173 -psychology clinic 0.071 0.999 0.235 0.040 0.596 2.612 0.053 0.045 0.081 0.121 -psychology doctor 0.077 1.073 0.333 0.076 1.593 3.724 0.165 0.059 0.106 0.161 -psychology mind 0.143 1.692 0.588 0.285 3.292 5.771 0.374 0.083 0.231 0.355 -psychology health 0.083 1.153 0.267 0.060 0.596 2.612 0.060 0.051 0.094 0.140 -psychology science 0.500 2.944 0.941 0.818 7.187 8.052 0.847 0.279 0.833 0.858 -psychology discipline 0.333 2.539 0.875 0.669 5.589 7.972 0.728 0.193 0.635 0.748 -psychology cognition 0.167 1.846 0.615 0.348 2.645 5.359 0.426 0.123 0.265 0.398 -planet star 0.333 2.539 0.857 0.667 6.932 8.564 0.874 0.334 0.701 0.772 -planet constellation 0.250 2.251 0.769 0.540 4.940 8.119 0.592 0.128 0.501 0.671 -planet moon 0.250 2.251 0.800 0.546 6.932 8.564 0.781 0.204 0.610 0.693 -planet sun 0.250 2.251 0.800 0.546 6.932 8.564 0.810 0.236 0.610 0.693 -planet galaxy 0.125 1.558 0.632 0.245 2.224 1.480 0.197 0.057 0.190 0.166 -planet space 0.143 1.692 0.533 0.251 1.532 1.227 0.191 0.084 0.182 0.171 -planet astronomer 0.167 1.846 0.632 0.367 2.334 1.540 0.242 0.070 0.252 0.220 -precedent example 0.500 2.944 0.923 0.815 8.077 14.581 0.817 0.217 0.858 0.963 -precedent information 0.333 2.539 0.833 0.659 5.081 9.032 0.606 0.131 0.608 0.790 -precedent cognition 0.250 2.251 0.727 0.520 2.645 5.359 0.369 0.100 0.376 0.524 -precedent law 0.500 2.944 0.909 0.805 8.372 10.983 0.839 0.238 0.866 0.921 -precedent collection 0.333 2.539 0.800 0.635 5.053 7.853 0.608 0.133 0.607 0.743 -precedent group 0.250 2.251 0.667 0.458 2.855 3.353 0.396 0.103 0.387 0.413 -precedent antecedent 0.167 1.846 0.615 0.348 3.848 6.513 0.305 0.054 0.321 0.461 -cup coffee 0.167 1.846 0.762 0.368 6.693 9.625 0.651 0.122 0.471 0.631 -cup tableware 0.333 2.539 0.889 0.670 7.490 8.640 0.840 0.260 0.727 0.775 -cup article 0.200 2.028 0.750 0.447 7.332 8.640 0.830 0.250 0.562 0.632 -cup artifact 0.250 2.251 0.769 0.540 2.306 2.729 0.365 0.111 0.358 0.380 -cup object 0.167 1.846 0.545 0.307 2.620 4.436 0.261 0.100 0.211 0.212 -cup drink 0.200 2.028 0.800 0.449 6.693 10.132 0.782 0.211 0.527 0.657 -cup food 0.167 1.846 0.667 0.362 5.279 7.383 0.672 0.163 0.394 0.510 -cup substance 0.143 1.692 0.571 0.285 4.615 6.858 0.614 0.147 0.318 0.435 -cup liquid 0.167 1.846 0.737 0.367 6.444 8.429 0.764 0.201 0.457 0.567 -jaguar cat 0.500 2.944 0.966 0.819 9.841 12.278 0.880 0.272 0.900 0.939 -jaguar car 0.059 0.804 0.333 0.039 1.532 1.227 0.152 0.055 0.081 0.076 -energy secretary 0.091 1.240 0.286 0.073 0.802 0.929 0.102 0.066 0.107 0.110 -energy laboratory 0.100 1.335 0.308 0.089 0.802 0.929 0.093 0.060 0.117 0.120 -computer laboratory 0.111 1.440 0.429 0.168 1.290 0.955 0.134 0.056 0.143 0.134 -weapon secret 0.091 1.240 0.286 0.073 0.596 2.612 0.067 0.063 0.103 0.152 -FBI fingerprint 0.077 1.073 0.250 0.049 0.596 2.612 0.051 0.043 0.087 0.130 -FBI investigation 0.067 0.930 0.222 0.033 0.596 2.612 0.064 0.054 0.075 0.113 -investigation effort 0.250 2.251 0.824 0.548 4.806 8.647 0.693 0.190 0.493 0.697 -Mars water 0.111 1.440 0.500 0.191 1.532 2.612 0.155 0.066 0.150 0.166 -Mars scientist 0.100 1.335 0.471 0.157 1.532 1.227 0.176 0.066 0.135 0.127 -news report 0.500 2.944 0.923 0.815 7.743 12.441 0.989 0.849 0.849 0.941 -canyon landscape 0.083 1.153 0.353 0.092 1.290 0.955 0.114 0.047 0.108 0.101 -image surface 0.200 2.028 0.714 0.442 3.292 5.771 0.378 0.099 0.295 0.398 -discovery space 0.143 1.692 0.400 0.162 0.596 2.612 0.089 0.076 0.160 0.230 -water seepage 0.083 1.153 0.250 0.049 0.596 2.612 0.056 0.050 0.087 0.130 -sign recess 0.200 2.028 0.750 0.447 3.979 4.386 0.438 0.089 0.378 0.399 -Wednesday news 0.083 1.153 0.267 0.060 0.596 2.612 0.067 0.056 0.094 0.140 -mile kilometer 0.250 2.251 0.800 0.546 6.061 13.887 0.579 0.102 0.563 0.881 -territory surface 0.167 1.846 0.615 0.348 3.292 5.771 0.557 0.173 0.281 0.355 -atmosphere landscape 0.111 1.440 0.500 0.191 3.012 2.612 0.299 0.066 0.197 0.176 -president medal 0.077 1.073 0.235 0.040 0.596 2.612 0.063 0.054 0.081 0.121 -war troops 0.100 1.335 0.308 0.089 0.596 2.612 0.071 0.060 0.113 0.166 -record number 0.500 2.944 0.909 0.805 5.819 9.297 0.796 0.251 0.726 0.868 -skin eye 0.167 1.846 0.667 0.362 4.402 7.536 0.490 0.098 0.276 0.434 -Japanese American 0.250 2.251 0.632 0.549 6.424 12.138 0.695 0.156 0.565 0.730 -theater history 0.143 1.692 0.500 0.251 1.593 3.724 0.182 0.065 0.192 0.277 -prejudice recognition 0.167 1.846 0.667 0.362 6.039 10.659 0.555 0.094 0.435 0.683 -decoration valor 0.083 1.153 0.267 0.060 0.596 2.612 0.052 0.050 0.094 0.140 -century year 0.333 2.539 0.833 0.659 3.800 7.890 0.484 0.110 0.539 0.744 -century nation 0.125 1.558 0.364 0.132 0.596 2.612 0.083 0.070 0.140 0.204 -delay racism 0.143 1.692 0.625 0.296 2.332 4.982 0.211 0.054 0.219 0.336 -delay news 0.125 1.558 0.533 0.233 2.036 3.923 0.213 0.062 0.184 0.255 -minister party 0.167 1.846 0.632 0.367 2.334 3.923 0.259 0.070 0.252 0.220 -peace plan 0.100 1.335 0.308 0.089 0.596 2.612 0.076 0.065 0.113 0.166 -minority peace 0.167 1.846 0.615 0.348 3.140 6.399 0.303 0.065 0.287 0.455 -attempt peace 0.100 1.335 0.308 0.089 0.596 2.612 0.077 0.065 0.113 0.166 -government crisis 0.143 1.692 0.571 0.285 2.036 3.923 0.236 0.070 0.208 0.286 -deployment departure 0.200 2.028 0.714 0.442 2.332 4.982 0.212 0.054 0.296 0.432 -deployment withdrawal 0.200 2.028 0.778 0.449 8.772 11.808 0.650 0.096 0.639 0.777 -energy crisis 0.143 1.692 0.625 0.296 4.235 6.780 0.443 0.086 0.300 0.431 -announcement news 0.200 2.028 0.667 0.425 3.848 6.513 0.490 0.111 0.371 0.517 -announcement effort 0.100 1.335 0.308 0.089 0.596 2.612 0.085 0.072 0.113 0.166 -stroke hospital 0.091 1.240 0.400 0.086 1.532 2.612 0.155 0.063 0.105 0.152 -disability death 0.125 1.558 0.533 0.233 3.140 6.399 0.336 0.074 0.224 0.373 -victim emergency 0.100 1.335 0.444 0.128 1.532 1.227 0.159 0.058 0.123 0.116 -treatment recovery 0.250 2.251 0.769 0.540 2.332 4.982 0.279 0.076 0.359 0.503 -journal association 0.143 1.692 0.400 0.162 0.802 2.612 0.081 0.059 0.160 0.230 -doctor personnel 0.083 1.153 0.267 0.060 0.596 2.612 0.076 0.066 0.094 0.140 -doctor liability 0.083 1.153 0.267 0.060 0.596 2.612 0.065 0.056 0.094 0.140 -liability insurance 0.143 1.692 0.625 0.296 4.235 6.780 0.360 0.062 0.300 0.431 -school center 0.333 2.539 0.875 0.669 5.715 5.359 0.669 0.150 0.642 0.591 -reason hypertension 0.111 1.440 0.556 0.199 4.235 6.780 0.385 0.069 0.243 0.362 -reason criterion 0.125 1.558 0.533 0.233 2.645 5.359 0.316 0.080 0.205 0.321 -hundred percent 0.077 1.073 0.250 0.049 0.596 2.612 0.068 0.058 0.087 0.130 -Harvard Yale 0.333 2.539 0.917 0.670 9.922 7.370 0.860 0.237 0.821 0.721 -hospital infrastructure 0.083 1.153 0.267 0.060 0.596 2.612 0.061 0.052 0.094 0.140 -death row 0.125 1.558 0.500 0.206 2.332 5.951 0.267 0.076 0.193 0.350 -death inmate 0.091 1.240 0.286 0.073 0.802 0.929 0.080 0.051 0.107 0.110 -life death 0.333 2.539 0.857 0.667 7.411 7.001 0.888 0.349 0.723 0.582 -life term 0.333 2.539 0.857 0.667 6.494 14.581 0.905 0.423 0.680 0.928 -word similarity 0.111 1.440 0.455 0.108 3.292 5.771 0.335 0.071 0.148 0.232 -board recommendation 0.083 1.153 0.267 0.060 0.596 2.612 0.068 0.057 0.094 0.140 -OPEC country 0.143 1.692 0.625 0.296 3.907 3.505 0.000 0.000 0.285 0.267 -peace atmosphere 0.200 2.028 0.714 0.442 4.235 6.780 0.501 0.106 0.391 0.532 -peace insurance 0.250 2.251 0.824 0.548 8.459 13.887 0.842 0.256 0.688 0.881 -territory kilometer 0.077 1.073 0.250 0.049 0.596 2.612 0.052 0.058 0.087 0.130 -travel activity 0.167 1.846 0.667 0.362 2.332 4.982 0.500 0.177 0.252 0.378 -competition price 0.125 1.558 0.545 0.206 3.037 5.511 0.321 0.072 0.220 0.328 -consumer energy 0.100 1.335 0.308 0.089 0.802 0.929 0.114 0.074 0.117 0.120 -car flight 0.125 1.558 0.588 0.243 2.306 2.729 0.256 0.070 0.193 0.208 -credit card 0.167 1.846 0.615 0.348 4.004 6.513 0.429 0.110 0.321 0.461 -credit information 0.250 2.251 0.727 0.520 4.934 7.423 0.551 0.125 0.440 0.588 -hotel reservation 0.091 1.240 0.375 0.113 1.290 0.955 0.141 0.060 0.118 0.110 -registration arrangement 0.200 2.028 0.667 0.375 3.144 5.519 0.359 0.084 0.321 0.363 -arrangement accommodation 0.143 1.692 0.667 0.300 4.385 9.003 0.410 0.093 0.252 0.482 -type kind 0.500 2.944 0.947 0.819 6.421 6.450 0.896 0.402 0.807 0.808 -arrival hotel 0.100 1.335 0.471 0.157 1.532 1.227 0.160 0.059 0.135 0.127 -bed closet 0.250 2.251 0.842 0.549 6.616 10.855 0.739 0.176 0.593 0.790 -closet clothes 0.125 1.558 0.588 0.243 2.306 2.729 0.245 0.066 0.193 0.208 -situation conclusion 0.143 1.692 0.625 0.296 2.332 4.982 0.346 0.102 0.219 0.336 -situation isolation 0.250 2.251 0.727 0.520 3.140 6.399 0.403 0.097 0.402 0.582 -impartiality interest 0.111 1.440 0.400 0.138 1.593 3.724 0.156 0.055 0.137 0.203 -direction combination 0.143 1.692 0.667 0.300 3.144 5.519 0.334 0.088 0.252 0.363 -street place 0.167 1.846 0.615 0.348 3.140 6.399 0.366 0.084 0.287 0.455 -street avenue 0.500 2.944 0.947 0.819 8.670 8.139 0.913 0.377 0.874 0.860 -street block 0.167 1.846 0.667 0.362 2.855 5.951 0.306 0.087 0.251 0.350 -street children 0.100 1.335 0.471 0.157 1.532 1.227 0.198 0.074 0.135 0.127 -listing proximity 0.077 1.073 0.316 0.062 1.593 3.724 0.135 0.057 0.099 0.150 -listing category 0.111 1.440 0.375 0.113 1.593 3.724 0.189 0.076 0.125 0.187 -cell phone 0.333 2.539 0.900 0.670 8.723 10.943 0.959 0.575 0.778 0.842 -production hike 0.125 1.558 0.588 0.243 2.332 4.982 0.232 0.064 0.194 0.303 -benchmark index 0.250 2.251 0.769 0.540 6.110 8.579 0.514 0.080 0.566 0.693 -media trading 0.143 1.692 0.625 0.296 2.332 4.982 0.231 0.060 0.219 0.336 -media gain 0.143 1.692 0.500 0.251 3.037 5.951 0.369 0.088 0.220 0.386 -dividend payment 0.333 2.539 0.889 0.670 6.737 13.664 0.726 0.164 0.692 0.913 -dividend calculation 0.091 1.240 0.286 0.073 0.596 2.612 0.062 0.052 0.103 0.152 -calculation computation 1.000 3.638 1.000 1.000 7.760 10.956 1.000 1.000 1.000 1.000 -currency market 0.100 1.335 0.308 0.089 0.596 2.612 0.080 0.068 0.113 0.166 -OPEC oil 0.071 0.999 0.235 0.040 0.596 2.612 0.000 0.000 0.081 0.121 -oil stock 0.111 1.440 0.571 0.199 5.118 9.717 0.524 0.097 0.259 0.493 -announcement production 0.143 1.692 0.500 0.251 2.620 4.436 0.301 0.076 0.230 0.310 -announcement warning 0.167 1.846 0.615 0.348 3.848 6.513 0.457 0.099 0.321 0.461 -profit warning 0.091 1.240 0.286 0.073 0.596 2.612 0.074 0.063 0.103 0.152 -profit loss 0.167 1.846 0.706 0.366 6.161 13.887 0.770 0.213 0.442 0.816 -dollar yen 0.250 2.251 0.800 0.546 6.875 7.001 0.610 0.102 0.607 0.284 -dollar buck 1.000 3.638 1.000 1.000 9.284 9.721 1.000 1.000 1.000 1.000 -dollar profit 0.100 1.335 0.308 0.089 0.596 2.612 0.072 0.061 0.113 0.166 -dollar loss 0.111 1.440 0.333 0.108 0.596 2.612 0.069 0.059 0.125 0.183 -network hardware 0.250 2.251 0.800 0.546 3.258 3.625 0.293 0.060 0.408 0.428 -phone equipment 0.333 2.539 0.875 0.669 5.268 9.016 0.753 0.224 0.618 0.789 -equipment maker 0.111 1.440 0.500 0.191 1.532 1.227 0.234 0.091 0.150 0.141 -five month 0.125 1.558 0.462 0.206 2.691 7.001 0.374 0.100 0.207 0.405 -report gain 0.125 1.558 0.364 0.132 0.596 2.612 0.084 0.071 0.140 0.204 -liquid water 0.500 2.944 0.933 0.818 8.115 14.581 0.994 0.916 0.859 0.963 -baseball season 0.067 0.930 0.222 0.033 0.596 2.612 0.069 0.059 0.075 0.113 -game victory 0.143 1.692 0.571 0.285 2.036 3.923 0.264 0.081 0.208 0.286 -game team 0.100 1.335 0.308 0.089 0.596 2.612 0.084 0.071 0.113 0.166 -marathon sprint 0.091 1.240 0.500 0.133 2.332 4.982 0.190 0.048 0.144 0.233 -game series 0.333 2.539 0.857 0.667 6.609 5.809 0.699 0.150 0.686 0.646 -game defeat 0.143 1.692 0.571 0.285 2.036 3.923 0.235 0.070 0.208 0.286 -seven series 0.111 1.440 0.375 0.113 3.037 5.511 0.316 0.071 0.165 0.255 -seafood sea 0.125 1.558 0.364 0.132 0.802 0.929 0.085 0.055 0.146 0.150 -seafood food 0.500 2.944 0.909 0.805 6.109 8.084 0.790 0.235 0.796 0.859 -seafood lobster 0.333 2.539 0.857 0.667 9.360 10.720 0.809 0.185 0.801 0.845 -lobster food 0.250 2.251 0.769 0.540 6.109 8.084 0.615 0.115 0.566 0.669 -lobster wine 0.091 1.240 0.375 0.113 3.337 5.761 0.297 0.059 0.174 0.266 -food preparation 0.143 1.692 0.571 0.285 3.337 5.761 0.461 0.114 0.231 0.355 -video archive 0.111 1.440 0.556 0.199 2.306 2.729 0.206 0.053 0.173 0.187 -start year 0.167 1.846 0.545 0.307 2.691 7.001 0.385 0.104 0.267 0.488 -start match 0.143 1.692 0.571 0.285 2.691 7.001 0.307 0.078 0.233 0.443 -game round 0.333 2.539 0.889 0.670 6.964 10.582 0.744 0.173 0.662 0.841 -boxing round 0.143 1.692 0.727 0.301 7.433 8.467 0.688 0.129 0.467 0.524 -championship tournament 0.333 2.539 0.857 0.667 6.609 5.809 0.633 0.115 0.686 0.646 -line insurance 0.200 2.028 0.714 0.442 5.176 10.997 0.559 0.121 0.442 0.744 -day summer 0.250 2.251 0.769 0.540 3.800 7.890 0.474 0.106 0.438 0.660 -summer drought 0.250 2.251 0.769 0.540 3.800 7.890 0.351 0.066 0.438 0.660 -summer nature 0.111 1.440 0.333 0.108 0.596 2.612 0.072 0.061 0.125 0.183 -day dawn 0.333 2.539 0.833 0.659 3.800 7.890 0.429 0.090 0.539 0.744 -nature environment 0.167 1.846 0.545 0.307 2.298 5.951 0.336 0.099 0.250 0.430 -environment ecology 0.500 2.944 0.923 0.815 6.464 9.110 0.000 0.000 0.809 0.884 -nature man 0.250 2.251 0.667 0.458 2.283 2.612 0.301 0.086 0.357 0.319 -man woman 0.333 2.539 0.706 0.670 4.910 4.037 0.787 0.273 0.599 0.552 -man governor 0.200 2.028 0.667 0.449 3.258 3.625 0.402 0.093 0.296 0.261 -murder manslaughter 0.333 2.539 0.917 0.670 8.560 9.849 0.785 0.176 0.772 0.818 -soap opera 0.071 0.999 0.235 0.040 0.802 2.612 0.075 0.048 0.081 0.121 -opera performance 0.100 1.335 0.308 0.089 0.802 2.612 0.084 0.057 0.117 0.152 -life lesson 0.125 1.558 0.429 0.168 2.620 4.436 0.310 0.079 0.183 0.252 -focus life 0.143 1.692 0.533 0.251 3.140 6.399 0.371 0.086 0.224 0.373 -production crew 0.125 1.558 0.364 0.132 0.596 2.612 0.070 0.065 0.140 0.204 -television film 0.200 2.028 0.778 0.449 7.021 9.016 0.720 0.155 0.545 0.516 -lover quarrel 0.077 1.073 0.400 0.086 1.532 1.227 0.169 0.062 0.105 0.099 -viewer serial 0.091 1.240 0.500 0.133 2.306 2.729 0.212 0.061 0.143 0.155 -population development 0.143 1.692 0.727 0.301 5.654 6.232 0.642 0.137 0.370 0.401 -morality importance 0.200 2.028 0.667 0.425 3.965 9.379 0.583 0.150 0.377 0.670 -morality marriage 0.167 1.846 0.545 0.307 2.298 5.951 0.306 0.088 0.250 0.430 -Mexico Brazil 0.200 2.028 0.800 0.449 6.134 7.312 0.495 0.074 0.496 0.561 -gender equality 0.125 1.558 0.462 0.206 2.298 5.951 0.234 0.062 0.193 0.350 -change attitude 0.167 1.846 0.545 0.307 3.037 5.951 0.350 0.126 0.283 0.406 -family planning 0.100 1.335 0.308 0.089 0.596 2.612 0.082 0.070 0.113 0.166 -opera industry 0.091 1.240 0.286 0.073 0.596 2.612 0.065 0.055 0.103 0.152 -sugar approach 0.091 1.240 0.286 0.073 0.802 2.612 0.087 0.056 0.107 0.140 -practice institution 0.333 2.539 0.833 0.659 7.355 13.077 0.871 0.315 0.721 0.902 -ministry culture 0.125 1.558 0.533 0.233 3.336 4.982 0.314 0.064 0.231 0.235 -problem challenge 0.167 1.846 0.615 0.348 3.848 6.513 0.421 0.086 0.287 0.455 -size prominence 0.167 1.846 0.615 0.348 3.140 6.399 0.312 0.074 0.287 0.455 -country citizen 0.111 1.440 0.429 0.168 1.290 0.955 0.178 0.077 0.143 0.134 -development issue 0.250 2.251 0.750 0.458 4.089 9.717 0.567 0.138 0.454 0.745 -experience music 0.167 1.846 0.615 0.348 2.645 5.359 0.329 0.086 0.240 0.324 -music project 0.250 2.251 0.800 0.546 3.144 5.519 0.432 0.108 0.402 0.533 -glass metal 0.167 1.846 0.545 0.307 3.337 5.761 0.454 0.111 0.296 0.420 -aluminum metal 0.500 2.944 0.933 0.818 5.442 13.077 0.667 0.155 0.771 0.949 -chance credibility 0.167 1.846 0.500 0.251 2.298 5.951 0.268 0.074 0.218 0.386 -exhibit memorabilia 0.111 1.440 0.429 0.168 2.620 4.436 0.214 0.049 0.183 0.252 -rock jazz 0.333 2.539 0.875 0.669 8.409 9.683 0.864 0.275 0.766 0.813 -museum theater 0.143 1.692 0.625 0.296 2.306 2.729 0.248 0.067 0.218 0.235 -observation architecture 0.167 1.846 0.706 0.366 3.144 5.519 0.295 0.067 0.287 0.407 -space world 0.200 2.028 0.571 0.307 2.298 5.951 0.334 0.099 0.222 0.309 -preservation world 0.143 1.692 0.588 0.243 4.235 6.780 0.445 0.086 0.269 0.393 -admission ticket 0.143 1.692 0.571 0.285 3.848 6.513 0.360 0.068 0.282 0.416 -shower thunderstorm 0.143 1.692 0.700 0.301 6.561 10.819 0.000 0.000 0.419 0.651 -shower flood 0.125 1.558 0.636 0.243 4.936 10.318 0.451 0.077 0.273 0.556 -disaster area 0.111 1.440 0.500 0.191 3.140 6.399 0.353 0.080 0.201 0.343 -governor office 0.100 1.335 0.526 0.163 2.306 2.729 0.250 0.067 0.157 0.170 -architecture century 0.100 1.335 0.308 0.089 0.596 2.612 0.063 0.054 0.113 0.166 diff --git a/sematch/eval/word_similarity/results_original/noun_mc.txt b/sematch/eval/word_similarity/results_original/noun_mc.txt deleted file mode 100644 index 1e32956..0000000 --- a/sematch/eval/word_similarity/results_original/noun_mc.txt +++ /dev/null @@ -1,31 +0,0 @@ -word1 word2 path lch wup li res lin jcn wpath -car automobile 1.000 3.638 1.000 1.000 7.591 1.000 1.000 1.000 -gem jewel 1.000 3.638 1.000 1.000 12.068 1.000 1.000 1.000 -journey voyage 0.500 2.944 0.952 0.819 6.826 0.778 0.204 0.821 -boy lad 0.500 2.944 0.947 0.819 8.399 0.831 0.226 0.867 -coast shore 0.500 2.944 0.909 0.805 9.416 0.963 0.582 0.891 -asylum madhouse 0.500 2.944 0.952 0.819 9.475 0.856 0.238 0.892 -magician wizard 1.000 3.638 0.667 1.000 11.981 1.000 1.000 1.000 -midday noon 1.000 3.638 1.000 1.000 11.064 1.000 1.000 1.000 -furnace stove 0.100 1.335 0.526 0.163 2.306 0.228 0.060 0.157 -food fruit 0.100 1.335 0.400 0.138 1.593 0.161 0.084 0.137 -bird cock 0.500 2.944 0.952 0.819 7.678 0.774 0.182 0.847 -bird crane 0.250 2.251 0.870 0.549 7.678 0.748 0.162 0.649 -tool implement 0.500 2.944 0.933 0.818 5.877 0.947 0.604 0.788 -brother monk 0.500 2.944 0.952 0.819 9.262 0.986 0.797 0.888 -lad brother 0.200 2.028 0.667 0.449 2.334 0.255 0.068 0.296 -crane implement 0.200 2.028 0.750 0.447 3.258 0.359 0.079 0.341 -journey car 0.056 0.747 0.105 0.000 0.000 0.000 0.065 0.056 -monk oracle 0.125 1.558 0.571 0.246 2.334 0.226 0.059 0.194 -cemetery woodland 0.111 1.440 0.429 0.168 1.290 0.123 0.052 0.143 -food rooster 0.063 0.865 0.211 0.027 0.802 0.092 0.059 0.074 -coast hill 0.200 2.028 0.667 0.425 5.885 0.599 0.113 0.482 -forest graveyard 0.111 1.440 0.429 0.168 1.290 0.123 0.052 0.143 -shore woodland 0.200 2.028 0.600 0.375 1.290 0.136 0.057 0.250 -monk slave 0.200 2.028 0.667 0.449 2.334 0.254 0.068 0.296 -coast forest 0.167 1.846 0.545 0.307 1.290 0.131 0.055 0.211 -lad wizard 0.200 2.028 0.667 0.449 2.334 0.255 0.068 0.296 -chord smile 0.091 1.240 0.375 0.113 2.620 0.246 0.059 0.152 -glass magician 0.125 1.558 0.471 0.206 2.283 0.214 0.056 0.192 -rooster voyage 0.042 0.460 0.080 0.000 0.000 0.000 0.042 0.042 -noon string 0.083 1.153 0.267 0.060 0.596 0.066 0.056 0.094 diff --git a/sematch/eval/word_similarity/results_original/noun_rg.txt b/sematch/eval/word_similarity/results_original/noun_rg.txt deleted file mode 100644 index ebedb28..0000000 --- a/sematch/eval/word_similarity/results_original/noun_rg.txt +++ /dev/null @@ -1,66 +0,0 @@ -word1 word2 path lch wup li res lin jcn wpath -gem jewel 1.000 3.638 1.000 1.000 12.068 1.000 1.000 1.000 -midday noon 1.000 3.638 1.000 1.000 11.064 1.000 1.000 1.000 -automobile car 1.000 3.638 1.000 1.000 7.591 1.000 1.000 1.000 -cemetery graveyard 1.000 3.638 1.000 1.000 11.288 1.000 1.000 1.000 -cushion pillow 0.500 2.944 0.933 0.818 11.288 0.977 0.650 0.925 -boy lad 0.500 2.944 0.947 0.819 8.399 0.831 0.226 0.867 -cock rooster 1.000 3.638 1.000 1.000 12.163 1.000 1.000 1.000 -implement tool 0.500 2.944 0.933 0.818 5.877 0.947 0.604 0.788 -forest woodland 1.000 3.638 0.833 0.995 9.614 1.000 1.000 1.000 -coast shore 0.500 2.944 0.909 0.805 9.416 0.963 0.582 0.891 -autograph signature 0.500 2.944 0.933 0.818 11.632 0.941 0.409 0.931 -journey voyage 0.500 2.944 0.952 0.819 6.826 0.778 0.204 0.821 -serf slave 0.250 2.251 0.632 0.549 9.090 0.795 0.176 0.717 -grin smile 1.000 3.638 1.000 0.999 9.583 1.000 1.000 1.000 -glass tumbler 0.500 2.944 0.941 0.818 9.442 0.813 0.188 0.892 -cord string 0.500 2.944 0.933 0.818 8.472 0.874 0.291 0.869 -hill mound 1.000 3.638 1.000 1.000 9.966 1.000 1.000 1.000 -magician wizard 1.000 3.638 0.667 1.000 11.981 1.000 1.000 1.000 -furnace stove 0.100 1.335 0.526 0.163 2.306 0.228 0.060 0.157 -asylum madhouse 0.500 2.944 0.952 0.819 9.475 0.856 0.238 0.892 -brother monk 0.500 2.944 0.952 0.819 9.262 0.986 0.797 0.888 -food fruit 0.100 1.335 0.400 0.138 1.593 0.161 0.084 0.137 -bird cock 0.500 2.944 0.952 0.819 7.678 0.774 0.182 0.847 -bird crane 0.250 2.251 0.870 0.549 7.678 0.748 0.162 0.649 -oracle sage 0.167 1.846 0.571 0.368 6.513 0.525 0.078 0.461 -sage wizard 0.167 1.846 0.632 0.367 2.334 0.205 0.053 0.252 -brother lad 0.200 2.028 0.667 0.449 2.334 0.255 0.068 0.296 -crane implement 0.200 2.028 0.750 0.447 3.258 0.359 0.079 0.341 -magician oracle 0.143 1.692 0.600 0.301 2.334 0.199 0.051 0.219 -glass jewel 0.143 1.692 0.588 0.251 2.306 0.237 0.063 0.217 -cemetery mound 0.091 1.240 0.375 0.113 1.290 0.129 0.054 0.118 -car journey 0.056 0.747 0.105 0.000 0.000 0.000 0.065 0.056 -hill woodland 0.167 1.846 0.545 0.307 1.290 0.141 0.060 0.211 -crane rooster 0.125 1.558 0.741 0.247 7.678 0.614 0.094 0.442 -furnace implement 0.125 1.558 0.588 0.243 2.306 0.271 0.075 0.193 -coast hill 0.200 2.028 0.667 0.425 5.885 0.599 0.113 0.482 -bird woodland 0.111 1.440 0.400 0.138 1.290 0.149 0.064 0.130 -shore voyage 0.067 0.930 0.125 0.000 0.000 0.000 0.047 0.067 -cemetery woodland 0.111 1.440 0.429 0.168 1.290 0.123 0.052 0.143 -food rooster 0.063 0.865 0.211 0.027 0.802 0.092 0.059 0.074 -forest graveyard 0.111 1.440 0.429 0.168 1.290 0.123 0.052 0.143 -lad wizard 0.200 2.028 0.667 0.449 2.334 0.255 0.068 0.296 -mound shore 0.200 2.028 0.667 0.425 5.885 0.565 0.099 0.482 -automobile cushion 0.111 1.440 0.600 0.201 3.258 0.356 0.078 0.205 -boy sage 0.167 1.846 0.632 0.367 2.334 0.220 0.057 0.252 -monk oracle 0.125 1.558 0.571 0.246 2.334 0.226 0.059 0.194 -shore woodland 0.200 2.028 0.600 0.375 1.290 0.136 0.057 0.250 -grin lad 0.083 1.153 0.154 0.000 0.000 0.000 0.051 0.083 -coast forest 0.167 1.846 0.545 0.307 1.290 0.131 0.055 0.211 -asylum cemetery 0.083 1.153 0.353 0.092 1.290 0.124 0.052 0.108 -monk slave 0.200 2.028 0.667 0.449 2.334 0.254 0.068 0.296 -cushion jewel 0.143 1.692 0.625 0.296 2.306 0.223 0.059 0.218 -boy rooster 0.083 1.153 0.522 0.110 2.224 0.223 0.061 0.130 -glass magician 0.125 1.558 0.471 0.206 2.283 0.214 0.056 0.192 -graveyard madhouse 0.067 0.930 0.300 0.051 1.290 0.108 0.045 0.087 -asylum monk 0.091 1.240 0.444 0.128 1.532 0.164 0.060 0.123 -asylum fruit 0.143 1.692 0.625 0.296 2.306 0.237 0.068 0.218 -grin implement 0.077 1.073 0.143 0.000 0.000 0.000 0.061 0.077 -mound stove 0.143 1.692 0.632 0.296 3.258 0.301 0.071 0.228 -automobile wizard 0.077 1.073 0.400 0.086 1.532 0.181 0.067 0.105 -autograph shore 0.100 1.335 0.182 0.000 0.000 0.000 0.048 0.100 -fruit furnace 0.111 1.440 0.556 0.199 2.306 0.218 0.061 0.173 -noon string 0.083 1.153 0.267 0.060 0.596 0.066 0.056 0.094 -rooster voyage 0.042 0.460 0.080 0.000 0.000 0.000 0.042 0.042 -cord smile 0.091 1.240 0.286 0.073 0.596 0.054 0.052 0.103 diff --git a/sematch/eval/word_similarity/results_original/noun_simlex.txt b/sematch/eval/word_similarity/results_original/noun_simlex.txt deleted file mode 100644 index 4b5cc6a..0000000 --- a/sematch/eval/word_similarity/results_original/noun_simlex.txt +++ /dev/null @@ -1,667 +0,0 @@ -word1 word2 path lch wup li res lin jcn wpath -wife husband 0.333 2.539 0.600 0.670 7.852 0.883 0.325 0.743 -book text 0.500 2.944 0.952 0.819 7.349 0.892 0.359 0.838 -groom bride 0.333 2.539 0.909 0.670 10.423 0.917 0.346 0.837 -night day 0.333 2.539 0.833 0.659 5.886 0.769 0.220 0.650 -south north 0.333 2.539 0.875 0.669 7.099 0.850 0.285 0.709 -plane airport 0.111 1.440 0.556 0.199 2.306 0.252 0.068 0.173 -uncle aunt 0.200 2.028 0.600 0.449 5.710 0.532 0.091 0.472 -horse mare 0.500 2.944 0.968 0.819 7.739 0.804 0.209 0.849 -bottom top 0.333 2.539 0.900 0.670 8.255 0.871 0.290 0.759 -friend buddy 0.500 2.944 0.706 0.818 7.142 0.866 0.312 0.831 -student pupil 1.000 3.638 0.667 1.000 7.686 1.000 1.000 1.000 -world globe 1.000 3.638 1.000 1.000 7.690 1.000 1.000 1.000 -leg arm 0.333 2.539 0.889 0.670 8.054 0.884 0.322 0.751 -plane jet 0.500 2.944 0.960 0.819 8.871 0.894 0.323 0.879 -woman man 0.333 2.539 0.706 0.670 4.910 0.787 0.273 0.599 -horse colt 0.091 1.240 0.583 0.135 4.946 0.523 0.100 0.232 -actress actor 0.500 2.944 0.952 0.819 6.547 0.939 0.541 0.812 -teacher instructor 1.000 3.638 1.000 1.000 8.890 1.000 1.000 1.000 -movie film 1.000 3.638 1.000 1.000 7.817 1.000 1.000 1.000 -bird hawk 0.333 2.539 0.909 0.670 7.678 0.816 0.224 0.735 -word dictionary 0.056 0.747 0.105 0.000 0.000 0.000 0.056 0.056 -money salary 0.100 1.335 0.471 0.157 4.470 0.544 0.118 0.232 -dog cat 0.200 2.028 0.857 0.449 7.912 0.877 0.310 0.594 -area region 1.000 3.638 1.000 0.995 7.270 1.000 1.000 1.000 -navy army 0.333 2.539 0.889 0.670 7.896 0.798 0.200 0.744 -book literature 0.250 2.251 0.769 0.540 4.263 0.509 0.109 0.463 -clothes closet 0.125 1.558 0.588 0.243 2.306 0.245 0.066 0.193 -sunset sunrise 0.333 2.539 0.900 0.670 8.087 0.715 0.147 0.752 -child adult 0.333 2.539 0.750 0.669 2.334 0.397 0.124 0.457 -cow cattle 0.500 2.944 0.971 0.819 8.593 0.895 0.331 0.872 -book story 0.200 2.028 0.714 0.442 5.646 0.704 0.174 0.468 -winter summer 0.333 2.539 0.857 0.667 7.905 0.816 0.219 0.745 -taxi cab 1.000 3.638 1.000 1.000 10.882 1.000 1.000 1.000 -tree maple 0.333 2.539 0.909 0.670 7.765 0.771 0.178 0.739 -bed bedroom 0.167 1.846 0.667 0.362 3.979 0.413 0.081 0.258 -roof ceiling 1.000 3.638 1.000 1.000 9.645 1.000 1.000 1.000 -disease infection 0.250 2.251 0.842 0.549 6.715 0.743 0.177 0.599 -arm shoulder 0.333 2.539 0.875 0.669 6.866 0.735 0.168 0.698 -sheep lamb 0.200 2.028 0.667 0.449 4.946 0.457 0.078 0.296 -lady gentleman 0.200 2.028 0.600 0.449 4.910 0.495 0.091 0.428 -boat anchor 0.111 1.440 0.600 0.201 3.258 0.321 0.068 0.205 -priest monk 0.143 1.692 0.600 0.301 2.334 0.267 0.072 0.219 -toe finger 0.333 2.539 0.889 0.670 8.924 0.883 0.296 0.786 -river stream 0.500 2.944 0.909 0.805 6.762 0.986 0.841 0.819 -anger fury 0.500 2.944 0.933 0.818 8.418 0.886 0.315 0.867 -date calendar 0.111 1.440 0.400 0.138 2.855 0.279 0.064 0.174 -sea ocean 1.000 3.638 1.000 0.995 9.598 1.000 1.000 1.000 -second minute 1.000 3.638 1.000 0.999 7.712 1.000 1.000 1.000 -hand thumb 0.250 2.251 0.824 0.548 6.389 0.673 0.139 0.581 -wood log 0.500 2.944 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saint 0.167 1.846 0.632 0.367 2.334 0.214 0.055 0.252 -destruction construction 0.111 1.440 0.526 0.191 2.332 0.267 0.078 0.165 -crowd bunch 1.000 3.638 1.000 0.995 9.482 1.000 1.000 1.000 -beach reef 0.200 2.028 0.667 0.425 5.885 0.594 0.110 0.482 -man child 0.333 2.539 0.750 0.669 2.334 0.368 0.111 0.457 -bread cheese 0.250 2.251 0.750 0.546 6.880 0.644 0.116 0.607 -champion winner 0.250 2.251 0.706 0.549 5.395 0.495 0.083 0.526 -celebration ceremony 0.333 2.539 0.857 0.667 6.583 0.663 0.130 0.685 -menu order 0.125 1.558 0.533 0.233 3.848 0.400 0.080 0.252 -king princess 0.167 1.846 0.600 0.368 4.969 0.454 0.077 0.377 -wealth prestige 0.143 1.692 0.571 0.285 3.140 0.305 0.065 0.251 -endurance strength 0.500 2.944 0.909 0.805 7.143 0.873 0.326 0.831 -danger threat 0.500 2.944 0.889 0.775 8.956 0.931 0.428 0.881 -god priest 0.200 2.028 0.600 0.449 4.969 0.591 0.127 0.431 -men fraternity 0.200 2.028 0.667 0.375 3.907 0.397 0.078 0.324 -buddy companion 0.333 2.539 0.667 0.670 7.142 0.797 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0.801 0.206 0.740 -guy stud 0.333 2.539 0.900 0.670 7.381 0.658 0.115 0.667 -chicken rice 0.143 1.692 0.600 0.301 3.337 0.315 0.065 0.219 -box elevator 0.167 1.846 0.706 0.366 3.258 0.333 0.071 0.293 -butter potato 0.200 2.028 0.750 0.447 6.880 0.644 0.116 0.537 -apartment furniture 0.143 1.692 0.625 0.296 2.306 0.299 0.085 0.218 -lake swamp 0.125 1.558 0.364 0.132 0.802 0.077 0.049 0.146 -salad vinegar 0.111 1.440 0.556 0.199 5.279 0.432 0.067 0.289 -flower bulb 0.200 2.028 0.778 0.449 6.394 0.619 0.113 0.510 -cloud mist 0.250 2.251 0.824 0.548 9.114 0.879 0.284 0.718 -driver pilot 0.200 2.028 0.609 0.449 5.268 0.512 0.091 0.407 -sugar honey 0.333 2.539 0.900 0.670 10.355 0.927 0.380 0.834 -body shoulder 0.333 2.539 0.833 0.659 4.402 0.479 0.094 0.572 -idea image 0.250 2.251 0.769 0.540 3.292 0.533 0.147 0.410 -father brother 0.200 2.028 0.667 0.449 5.710 0.639 0.134 0.417 -moon planet 0.250 2.251 0.800 0.546 6.932 0.781 0.204 0.610 -ball costume 0.143 1.692 0.625 0.296 2.306 0.235 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5.267 0.709 0.188 0.618 -medium news 0.143 1.692 0.533 0.251 2.298 0.227 0.063 0.218 -winner candidate 0.333 2.539 0.750 0.669 2.334 0.269 0.073 0.457 -birthday date 0.500 2.944 0.923 0.815 9.295 0.886 0.294 0.888 -fee payment 0.200 2.028 0.778 0.449 5.973 0.770 0.219 0.487 -bible hymn 0.100 1.335 0.400 0.138 2.620 0.276 0.068 0.166 -exit doorway 0.143 1.692 0.625 0.296 2.306 0.263 0.072 0.218 -man sentry 0.143 1.692 0.600 0.301 2.334 0.299 0.084 0.219 -aisle hall 0.250 2.251 0.842 0.549 7.451 0.677 0.123 0.637 -whiskey gin 0.333 2.539 0.909 0.670 8.856 0.863 0.262 0.783 -blood marrow 0.125 1.558 0.545 0.138 3.337 0.317 0.072 0.190 -oil mink 0.100 1.335 0.571 0.164 5.118 0.451 0.074 0.240 -floor deck 0.250 2.251 0.824 0.548 6.854 0.729 0.164 0.606 -roof floor 0.333 2.539 0.923 0.670 8.031 0.867 0.288 0.750 -door floor 0.333 2.539 0.889 0.670 6.550 0.722 0.165 0.626 -shoulder head 0.250 2.251 0.769 0.540 6.133 0.679 0.147 0.471 -wagon carriage 0.333 2.539 0.800 0.670 6.452 0.688 0.146 0.678 -car carriage 0.500 2.944 0.952 0.819 8.094 0.875 0.302 0.859 -elbow ankle 0.200 2.028 0.778 0.449 8.886 0.764 0.154 0.645 -wealth fame 0.125 1.558 0.533 0.233 3.140 0.303 0.065 0.224 -sorrow shame 0.250 2.251 0.769 0.540 5.073 0.515 0.095 0.508 -administration management 0.500 2.944 0.941 0.818 7.380 0.961 0.628 0.838 -communication conversation 0.250 2.251 0.667 0.458 2.620 0.501 0.161 0.374 -pollution atmosphere 0.250 2.251 0.769 0.540 4.235 0.427 0.081 0.462 -anatomy biology 0.333 2.539 0.917 0.670 11.000 0.950 0.462 0.853 -college profession 0.250 2.251 0.769 0.540 5.967 0.652 0.136 0.558 -book topic 0.200 2.028 0.667 0.425 3.848 0.517 0.122 0.371 -formula equation 0.333 2.539 0.857 0.667 8.905 0.901 0.338 0.785 -book information 0.333 2.539 0.833 0.659 5.646 0.820 0.287 0.638 -boy partner 0.250 2.251 0.706 0.548 5.710 0.729 0.191 0.417 -sky universe 0.100 1.335 0.308 0.089 0.802 0.097 0.063 0.117 -population people 0.500 2.944 0.889 0.775 5.371 0.722 0.194 0.768 -college 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2.539 0.889 0.670 6.729 0.799 0.228 0.692 -people party 0.200 2.028 0.600 0.375 3.336 0.444 0.123 0.321 -boy brother 0.200 2.028 0.667 0.449 5.710 0.660 0.145 0.338 -root grass 0.167 1.846 0.632 0.367 2.334 0.281 0.081 0.252 -brow eye 0.167 1.846 0.667 0.362 4.402 0.456 0.087 0.348 -money pearl 0.091 1.240 0.286 0.073 0.596 0.066 0.056 0.103 -money diamond 0.100 1.335 0.400 0.138 3.037 0.302 0.067 0.180 -vehicle bus 0.250 2.251 0.824 0.548 5.922 0.722 0.180 0.547 -cab bus 0.333 2.539 0.923 0.670 7.591 0.711 0.139 0.731 -house barn 0.250 2.251 0.824 0.548 5.715 0.628 0.129 0.544 -finger palm 0.333 2.539 0.857 0.667 6.061 0.572 0.099 0.659 -car bridge 0.167 1.846 0.706 0.366 3.979 0.442 0.091 0.327 -effort difficulty 0.500 2.944 0.947 0.819 7.078 0.892 0.369 0.829 -fact insight 0.200 2.028 0.667 0.425 3.140 0.375 0.102 0.311 -job management 0.250 2.251 0.824 0.548 5.911 0.754 0.206 0.555 -cancer sickness 0.200 2.028 0.818 0.449 7.264 0.793 0.208 0.558 -word newspaper 0.143 1.692 0.571 0.285 3.611 0.442 0.099 0.272 -composer writer 0.143 1.692 0.600 0.301 2.334 0.346 0.102 0.219 -actor singer 0.250 2.251 0.667 0.549 5.718 0.820 0.286 0.544 -shelter hut 0.500 2.944 0.933 0.818 8.218 0.848 0.253 0.862 -bathroom kitchen 0.333 2.539 0.889 0.670 5.408 0.660 0.152 0.626 -cabin hut 0.167 1.846 0.706 0.366 3.979 0.358 0.065 0.327 -door kitchen 0.333 2.539 0.889 0.670 5.408 0.582 0.114 0.626 -value belief 0.250 2.251 0.800 0.546 4.004 0.525 0.136 0.449 -wisdom intelligence 0.250 2.251 0.769 0.540 4.861 0.631 0.149 0.497 -ignorance intelligence 0.200 2.028 0.667 0.425 2.645 0.311 0.078 0.311 -happiness luck 0.200 2.028 0.667 0.425 3.140 0.383 0.090 0.335 -idea scheme 0.333 2.539 0.857 0.667 4.004 0.656 0.192 0.550 -mood emotion 0.333 2.539 0.833 0.659 5.073 0.642 0.150 0.608 -happiness peace 0.200 2.028 0.714 0.442 5.073 0.540 0.104 0.437 -despair misery 0.250 2.251 0.769 0.540 5.073 0.467 0.079 0.508 -logic arithmetic 0.125 1.558 0.667 0.246 5.589 0.465 0.072 0.332 -denial confession 0.250 2.251 0.800 0.546 7.613 0.687 0.126 0.646 -argument criticism 0.167 1.846 0.615 0.348 4.385 0.506 0.104 0.321 -aggression hostility 1.000 3.638 1.000 0.999 11.098 1.000 1.000 1.000 -hysteria confusion 0.143 1.692 0.625 0.296 5.073 0.439 0.082 0.341 -chemistry theory 0.125 1.558 0.588 0.243 3.292 0.349 0.075 0.229 -trial verdict 0.167 1.846 0.737 0.367 6.685 0.631 0.114 0.471 -comfort safety 0.333 2.539 0.833 0.659 4.235 0.483 0.099 0.563 -confidence self 0.200 2.028 0.778 0.449 6.000 0.583 0.104 0.488 -vision perception 0.333 2.539 0.875 0.669 6.460 0.770 0.206 0.679 -era decade 0.333 2.539 0.833 0.659 3.800 0.468 0.104 0.539 -biography fiction 0.100 1.335 0.400 0.138 2.620 0.341 0.090 0.166 -discussion argument 0.500 2.944 0.923 0.815 7.114 0.846 0.278 0.830 -code symbol 0.200 2.028 0.600 0.375 2.620 0.463 0.141 0.310 -danger disease 0.143 1.692 0.625 0.296 4.235 0.488 0.101 0.300 -accident catastrophe 0.250 2.251 0.824 0.548 7.958 0.836 0.243 0.663 -journey trip 0.500 2.944 0.952 0.819 6.826 0.917 0.446 0.821 -activity movement 0.200 2.028 0.750 0.447 5.841 0.768 0.221 0.479 -gossip news 0.111 1.440 0.471 0.168 2.620 0.268 0.065 0.183 -father god 0.250 2.251 0.842 0.549 7.035 0.879 0.340 0.616 -action course 0.500 2.944 0.923 0.815 3.734 0.669 0.213 0.697 -fever illness 0.100 1.335 0.471 0.157 3.140 0.315 0.068 0.183 -aviation flight 0.500 2.944 0.952 0.819 8.435 0.968 0.643 0.868 -game action 0.250 2.251 0.800 0.546 3.258 0.436 0.142 0.402 -molecule air 0.200 2.028 0.571 0.442 3.886 0.422 0.086 0.373 -home state 0.250 2.251 0.800 0.546 4.752 0.685 0.186 0.490 -word literature 0.250 2.251 0.769 0.540 4.263 0.494 0.103 0.463 -adult guardian 0.250 2.251 0.706 0.548 2.334 0.390 0.120 0.359 -newspaper information 0.100 1.335 0.400 0.138 2.855 0.332 0.080 0.174 -communication television 0.083 1.153 0.154 0.000 0.000 0.000 0.074 0.083 -cousin uncle 0.250 2.251 0.632 0.549 5.710 0.551 0.097 0.544 -author reader 0.200 2.028 0.667 0.449 2.334 0.304 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0.576 0.098 0.471 -book article 0.333 2.539 0.857 0.667 5.958 0.715 0.174 0.654 -room cell 0.500 2.944 0.941 0.818 5.408 0.804 0.276 0.770 -box booth 0.200 2.028 0.800 0.449 6.616 0.586 0.097 0.523 -daughter kid 0.333 2.539 0.909 0.670 6.838 0.900 0.398 0.697 -limb leg 0.500 2.944 0.941 0.818 8.054 0.935 0.470 0.858 -liver lung 0.250 2.251 0.824 0.548 8.558 0.756 0.153 0.692 -classroom hallway 0.100 1.335 0.526 0.163 2.306 0.223 0.059 0.157 -mountain ledge 0.250 2.251 0.769 0.540 6.949 0.664 0.124 0.611 -car elevator 0.143 1.692 0.667 0.300 3.258 0.353 0.077 0.256 -bed couch 0.500 2.944 0.952 0.819 8.319 0.823 0.218 0.865 -clothes button 0.200 2.028 0.714 0.442 2.306 0.242 0.065 0.295 -clothes coat 0.250 2.251 0.800 0.546 6.125 0.654 0.134 0.511 -kidney organ 0.250 2.251 0.800 0.546 6.428 0.695 0.151 0.583 -apple sauce 0.077 1.073 0.333 0.076 3.337 0.315 0.064 0.149 -chicken steak 0.167 1.846 0.706 0.366 6.725 0.614 0.106 0.473 -car hose 0.111 1.440 0.556 0.199 2.306 0.245 0.066 0.173 -tobacco cigarette 0.333 2.539 0.800 0.670 9.104 0.977 0.701 0.792 -student professor 0.125 1.558 0.571 0.246 2.334 0.276 0.076 0.194 -baby daughter 0.250 2.251 0.870 0.549 6.838 0.754 0.183 0.605 -pipe cigar 0.067 0.930 0.222 0.033 0.802 0.081 0.052 0.075 -milk juice 0.333 2.539 0.875 0.669 7.061 0.643 0.116 0.707 -box cigar 0.071 0.999 0.235 0.040 0.802 0.078 0.050 0.084 -apartment hotel 0.200 2.028 0.750 0.447 3.979 0.463 0.098 0.378 -cup cone 0.250 2.251 0.824 0.548 6.394 0.583 0.098 0.581 -horse ox 0.111 1.440 0.750 0.202 7.024 0.723 0.157 0.375 -throat nose 0.250 2.251 0.800 0.546 6.676 0.650 0.122 0.597 -bone teeth 0.500 2.944 0.947 0.819 8.435 0.945 0.505 0.868 -bone elbow 0.143 1.692 0.625 0.296 4.402 0.439 0.082 0.308 -bacon bean 0.111 1.440 0.600 0.201 6.109 0.524 0.083 0.328 -cup jar 0.333 2.539 0.833 0.659 6.440 0.585 0.099 0.678 -proof fact 0.250 2.251 0.769 0.540 5.081 0.654 0.157 0.509 -appointment engagement 1.000 3.638 1.000 1.000 9.251 1.000 1.000 1.000 -birthday year 0.167 1.846 0.667 0.362 3.800 0.408 0.083 0.318 -word clue 0.200 2.028 0.714 0.442 5.819 0.684 0.157 0.478 -author creator 0.333 2.539 0.706 0.670 5.691 0.783 0.241 0.640 -atom carbon 0.250 2.251 0.800 0.546 4.693 0.453 0.096 0.487 -archbishop bishop 0.500 2.944 0.960 0.819 9.162 0.926 0.404 0.885 -letter paragraph 0.200 2.028 0.714 0.442 4.263 0.454 0.089 0.393 -page paragraph 0.083 1.153 0.250 0.049 0.596 0.058 0.049 0.087 -steeple chapel 0.167 1.846 0.706 0.366 3.979 0.349 0.063 0.327 -muscle bone 0.250 2.251 0.824 0.548 7.428 0.799 0.212 0.636 -muscle tongue 0.250 2.251 0.800 0.546 6.428 0.629 0.117 0.583 -boy soldier 0.125 1.558 0.571 0.246 2.334 0.297 0.083 0.194 -belly abdomen 1.000 3.638 1.000 0.995 10.217 1.000 1.000 1.000 -guy girl 0.200 2.028 0.632 0.449 4.910 0.568 0.118 0.428 -bed chair 0.200 2.028 0.800 0.449 6.616 0.731 0.170 0.523 -clothes jacket 0.200 2.028 0.762 0.447 6.125 0.622 0.118 0.440 -gun knife 0.333 2.539 0.900 0.670 6.763 0.770 0.198 0.693 -tin metal 0.500 2.944 0.933 0.818 5.442 0.615 0.128 0.771 -bottle container 0.333 2.539 0.875 0.669 5.206 0.702 0.184 0.615 -hen turkey 0.250 2.251 0.889 0.549 9.721 0.893 0.317 0.745 -meat bread 0.250 2.251 0.769 0.540 6.109 0.772 0.217 0.566 -arm bone 0.111 1.440 0.556 0.199 4.402 0.504 0.103 0.250 -neck spine 0.333 2.539 0.800 0.635 5.733 0.521 0.100 0.643 -apple lemon 0.250 2.251 0.857 0.549 9.614 0.811 0.183 0.681 -agony grief 0.143 1.692 0.625 0.296 5.081 0.479 0.083 0.341 -assignment task 0.500 2.944 0.941 0.818 6.380 0.855 0.321 0.801 -night dawn 0.333 2.539 0.857 0.659 7.269 0.843 0.270 0.628 -dinner soup 0.200 2.028 0.750 0.447 6.205 0.653 0.132 0.500 -calf bull 0.143 1.692 0.667 0.300 4.946 0.441 0.074 0.274 -snow storm 0.200 2.028 0.778 0.449 6.561 0.642 0.120 0.519 -nail hand 0.250 2.251 0.800 0.546 6.061 0.617 0.117 0.563 -dog horse 0.125 1.558 0.759 0.247 5.414 0.647 0.145 0.323 -arm neck 0.200 2.028 0.750 0.447 5.720 0.600 0.116 0.473 -ball glove 0.167 1.846 0.737 0.367 5.268 0.519 0.093 0.393 -flu fever 0.077 1.073 0.400 0.086 3.140 0.255 0.052 0.144 -fee salary 0.143 1.692 0.700 0.301 5.973 0.725 0.181 0.387 -nerve brain 0.111 1.440 0.556 0.199 4.402 0.416 0.075 0.250 -beast animal 1.000 3.638 1.000 0.999 4.946 1.000 1.000 1.000 -dinner chicken 0.091 1.240 0.353 0.092 3.337 0.332 0.069 0.161 -girl maid 0.500 2.944 0.952 0.819 7.544 0.828 0.242 0.843 -child boy 0.333 2.539 0.909 0.670 6.838 0.934 0.508 0.697 -alcohol wine 0.500 2.944 0.947 0.819 7.585 0.930 0.466 0.845 -nose mouth 0.250 2.251 0.800 0.546 6.676 0.669 0.132 0.597 -street car 0.111 1.440 0.556 0.199 2.306 0.284 0.079 0.173 -bell door 0.200 2.028 0.714 0.442 2.306 0.234 0.062 0.295 -box hat 0.125 1.558 0.588 0.243 2.332 0.254 0.069 0.193 -belief impression 1.000 3.638 1.000 0.999 7.374 1.000 1.000 1.000 -bias opinion 0.125 1.558 0.533 0.233 2.645 0.299 0.075 0.205 -attention awareness 0.167 1.846 0.667 0.362 4.385 0.578 0.135 0.347 -anger mood 0.250 2.251 0.769 0.540 5.073 0.559 0.111 0.508 -elegance style 0.500 2.944 0.909 0.805 8.343 0.935 0.463 0.865 -beauty age 0.167 1.846 0.545 0.307 2.298 0.282 0.079 0.250 -book theme 0.200 2.028 0.714 0.442 5.162 0.686 0.175 0.442 -friend mother 0.167 1.846 0.632 0.367 6.263 0.713 0.166 0.403 -vitamin iron 0.111 1.440 0.429 0.168 3.337 0.370 0.081 0.208 -car factory 0.125 1.558 0.632 0.245 3.979 0.440 0.090 0.258 -pact condition 0.200 2.028 0.714 0.442 4.934 0.575 0.120 0.429 -chapter choice 0.143 1.692 0.571 0.285 2.036 0.244 0.073 0.208 -arithmetic rhythm 0.091 1.240 0.500 0.133 3.292 0.282 0.056 0.173 -winner presence 0.100 1.335 0.353 0.092 1.290 0.152 0.065 0.108 -belief flower 0.111 1.440 0.333 0.108 0.596 0.083 0.071 0.125 -winner goal 0.100 1.335 0.471 0.157 1.532 0.180 0.069 0.135 -trick size 0.100 1.335 0.308 0.089 0.802 0.089 0.060 0.113 -choice vein 0.111 1.440 0.333 0.108 0.596 0.066 0.056 0.125 -hymn conquest 0.077 1.073 0.250 0.049 0.596 0.056 0.048 0.087 -endurance band 0.125 1.558 0.375 0.132 2.298 0.235 0.064 0.143 -jail choice 0.059 0.804 0.111 0.000 0.000 0.000 0.053 0.059 -condition boy 0.100 1.335 0.182 0.000 0.000 0.000 0.077 0.100 -flower endurance 0.111 1.440 0.333 0.108 0.596 0.064 0.055 0.125 -hole agreement 0.143 1.692 0.571 0.285 3.140 0.349 0.079 0.251 -doctor temper 0.083 1.153 0.267 0.060 0.596 0.062 0.053 0.094 -fraternity door 0.083 1.153 0.235 0.040 0.596 0.058 0.049 0.083 -task woman 0.091 1.240 0.286 0.073 0.596 0.081 0.074 0.103 -fraternity baseball 0.067 0.930 0.222 0.033 0.596 0.056 0.048 0.075 -cent size 0.100 1.335 0.308 0.089 0.596 0.074 0.063 0.113 -presence door 0.125 1.558 0.632 0.245 4.004 0.425 0.084 0.259 -mouse management 0.091 1.240 0.222 0.033 0.596 0.065 0.055 0.091 -task highway 0.067 0.930 0.125 0.000 0.000 0.000 0.060 0.067 -liquor century 0.091 1.240 0.286 0.073 0.596 0.072 0.061 0.103 -task straw 0.077 1.073 0.250 0.049 0.596 0.069 0.058 0.087 -island task 0.083 1.153 0.154 0.000 0.000 0.000 0.072 0.083 -night chapter 0.250 2.251 0.769 0.540 3.800 0.423 0.088 0.438 -pollution president 0.091 1.240 0.500 0.133 2.332 0.224 0.058 0.144 -gun trick 0.125 1.558 0.588 0.246 2.334 0.260 0.070 0.194 -bath trick 0.143 1.692 0.667 0.300 3.734 0.333 0.064 0.252 -diet apple 0.100 1.335 0.400 0.138 3.337 0.307 0.062 0.190 -cent wife 0.071 0.999 0.133 0.000 0.000 0.000 0.054 0.071 -chapter tail 0.125 1.558 0.462 0.206 2.691 0.257 0.060 0.207 -course stomach 0.111 1.440 0.333 0.108 0.802 0.088 0.057 0.130 -hymn straw 0.077 1.073 0.250 0.049 0.596 0.053 0.045 0.087 -dentist colonel 0.077 1.073 0.462 0.091 2.334 0.215 0.055 0.123 -wife straw 0.091 1.240 0.400 0.086 1.532 0.152 0.055 0.107 -hole wife 0.100 1.335 0.471 0.157 1.532 0.187 0.070 0.135 -pupil president 0.167 1.846 0.632 0.367 2.334 0.316 0.090 0.252 -bath wife 0.083 1.153 0.421 0.105 1.532 0.162 0.059 0.113 -people cent 0.111 1.440 0.333 0.108 0.596 0.083 0.071 0.125 -formula log 0.111 1.440 0.429 0.168 3.337 0.312 0.064 0.183 -woman fur 0.091 1.240 0.444 0.128 1.532 0.172 0.064 0.123 -apple sunshine 0.091 1.240 0.235 0.040 0.802 0.080 0.051 0.091 -gun dawn 0.100 1.335 0.471 0.157 2.036 0.222 0.065 0.149 -meal waist 0.111 1.440 0.333 0.108 0.802 0.080 0.051 0.130 -camera president 0.083 1.153 0.421 0.105 1.532 0.172 0.064 0.113 -liquor band 0.100 1.335 0.308 0.089 0.802 0.092 0.065 0.113 -stomach vein 0.200 2.028 0.714 0.442 4.402 0.419 0.076 0.400 -gun fur 0.111 1.440 0.556 0.199 2.306 0.239 0.064 0.173 -couch baseball 0.111 1.440 0.600 0.201 3.258 0.311 0.065 0.205 -worker camera 0.100 1.335 0.471 0.157 1.532 0.202 0.076 0.135 -deck mouse 0.125 1.558 0.632 0.245 3.258 0.289 0.059 0.228 -rice boy 0.143 1.692 0.600 0.301 2.334 0.249 0.066 0.219 -people gun 0.100 1.335 0.308 0.089 0.596 0.081 0.071 0.113 -cliff tail 0.125 1.558 0.429 0.168 1.290 0.131 0.062 0.146 -ankle window 0.077 1.073 0.250 0.049 0.802 0.078 0.050 0.091 -princess island 0.111 1.440 0.429 0.168 1.290 0.139 0.059 0.143 -container mouse 0.200 2.028 0.750 0.447 3.258 0.388 0.089 0.341 -wagon container 0.333 2.539 0.875 0.669 5.206 0.676 0.167 0.615 -people balloon 0.100 1.335 0.182 0.000 0.000 0.000 0.055 0.100 -dollar people 0.143 1.692 0.400 0.162 0.596 0.081 0.069 0.160 -bath balloon 0.143 1.692 0.625 0.296 3.258 0.291 0.059 0.218 -stomach bedroom 0.083 1.153 0.267 0.060 0.802 0.081 0.052 0.098 -bicycle bedroom 0.111 1.440 0.556 0.199 2.306 0.244 0.065 0.173 -log bath 0.125 1.558 0.632 0.245 3.258 0.298 0.061 0.228 -bowl tail 0.167 1.846 0.667 0.362 6.486 0.581 0.097 0.460 diff --git a/sematch/eval/word_similarity/results_original/noun_ws353-sim.txt b/sematch/eval/word_similarity/results_original/noun_ws353-sim.txt deleted file mode 100644 index c6b9c57..0000000 --- a/sematch/eval/word_similarity/results_original/noun_ws353-sim.txt +++ /dev/null @@ -1,202 +0,0 @@ -word1 word2 path lch wup li res lin jcn wpath -tiger cat 0.500 2.944 0.966 0.819 9.841 0.894 0.301 0.900 -tiger tiger 1.000 3.638 1.000 1.000 12.268 1.000 1.000 1.000 -plane car 0.143 1.692 0.727 0.301 5.922 0.719 0.178 0.385 -train car 0.167 1.846 0.737 0.367 5.769 0.679 0.155 0.420 -television radio 0.333 2.539 0.909 0.670 9.123 0.920 0.386 0.793 -media radio 0.250 2.251 0.824 0.548 7.021 0.832 0.261 0.615 -bread butter 0.250 2.251 0.750 0.546 6.880 0.711 0.152 0.607 -cucumber potato 0.250 2.251 0.824 0.548 9.442 0.659 0.103 0.686 -doctor nurse 0.250 2.251 0.870 0.549 7.930 0.835 0.242 0.662 -professor doctor 0.143 1.692 0.545 0.301 6.394 0.704 0.157 0.410 -student professor 0.125 1.558 0.571 0.246 2.334 0.276 0.076 0.194 -smart stupid 0.071 0.999 0.133 0.000 0.000 0.000 0.042 0.071 -wood forest 1.000 3.638 1.000 0.995 9.002 1.000 1.000 1.000 -money cash 0.333 2.539 0.875 0.669 6.445 0.789 0.230 0.678 -king queen 1.000 3.638 1.000 1.000 9.568 1.000 1.000 1.000 -king rook 0.333 2.539 0.909 0.670 8.505 0.772 0.166 0.769 -bishop rabbi 0.200 2.028 0.545 0.449 6.962 0.666 0.125 0.542 -fuck sex 0.333 2.539 0.857 0.667 6.690 0.770 0.200 0.690 -football soccer 0.500 2.944 0.880 0.819 11.064 0.906 0.303 0.922 -football basketball 0.333 2.539 0.900 0.670 8.716 0.754 0.149 0.771 -football tennis 0.167 1.846 0.783 0.368 8.716 0.770 0.161 0.583 -Arafat Jackson 0.250 2.251 0.571 0.549 6.737 0.000 0.000 0.600 -physics chemistry 0.333 2.539 0.900 0.670 8.716 0.818 0.205 0.778 -vodka gin 0.333 2.539 0.909 0.670 8.856 0.000 0.000 0.783 -vodka brandy 0.333 2.539 0.909 0.670 8.856 0.000 0.000 0.783 -car automobile 1.000 3.638 1.000 1.000 7.591 1.000 1.000 1.000 -gem jewel 1.000 3.638 1.000 1.000 12.068 1.000 1.000 1.000 -journey voyage 0.500 2.944 0.952 0.819 6.826 0.778 0.204 0.821 -boy lad 0.500 2.944 0.947 0.819 8.399 0.831 0.226 0.867 -coast shore 0.500 2.944 0.909 0.805 9.416 0.963 0.582 0.891 -asylum madhouse 0.500 2.944 0.952 0.819 9.475 0.856 0.238 0.892 -magician wizard 1.000 3.638 0.667 1.000 11.981 1.000 1.000 1.000 -midday noon 1.000 3.638 1.000 1.000 11.064 1.000 1.000 1.000 -furnace stove 0.100 1.335 0.526 0.163 2.306 0.228 0.060 0.157 -food fruit 0.100 1.335 0.400 0.138 1.593 0.161 0.084 0.137 -bird cock 0.500 2.944 0.952 0.819 7.678 0.774 0.182 0.847 -bird crane 0.250 2.251 0.870 0.549 7.678 0.748 0.162 0.649 -food rooster 0.063 0.865 0.211 0.027 0.802 0.092 0.059 0.074 -money dollar 0.200 2.028 0.778 0.449 6.445 0.722 0.172 0.513 -money currency 0.500 2.944 0.933 0.818 6.445 0.864 0.336 0.808 -tiger jaguar 0.333 2.539 0.933 0.670 9.841 0.797 0.167 0.818 -tiger feline 0.333 2.539 0.929 0.670 8.663 0.832 0.222 0.776 -tiger carnivore 0.250 2.251 0.889 0.549 7.912 0.788 0.190 0.661 -tiger mammal 0.167 1.846 0.800 0.368 5.406 0.615 0.129 0.401 -tiger animal 0.250 2.251 0.800 0.546 4.946 0.578 0.122 0.354 -tiger organism 0.333 2.539 0.857 0.667 2.224 0.309 0.091 0.451 -tiger fauna 0.250 2.251 0.800 0.546 4.946 0.578 0.122 0.354 -psychology psychiatry 0.143 1.692 0.727 0.301 7.187 0.655 0.117 0.453 -psychology science 0.500 2.944 0.941 0.818 7.187 0.847 0.279 0.833 -psychology discipline 0.333 2.539 0.875 0.669 5.589 0.728 0.193 0.635 -planet star 0.333 2.539 0.857 0.667 6.932 0.874 0.334 0.701 -planet moon 0.250 2.251 0.800 0.546 6.932 0.781 0.204 0.610 -planet sun 0.250 2.251 0.800 0.546 6.932 0.810 0.236 0.610 -precedent example 0.500 2.944 0.923 0.815 8.077 0.817 0.217 0.858 -precedent antecedent 0.167 1.846 0.615 0.348 3.848 0.305 0.054 0.321 -cup tableware 0.333 2.539 0.889 0.670 7.490 0.840 0.260 0.727 -cup artifact 0.250 2.251 0.769 0.540 2.306 0.365 0.111 0.358 -cup object 0.167 1.846 0.545 0.307 2.620 0.261 0.100 0.211 -cup entity 0.167 1.846 0.286 0.000 0.000 0.000 0.088 0.167 -jaguar cat 0.500 2.944 0.966 0.819 9.841 0.880 0.272 0.900 -jaguar car 0.059 0.804 0.333 0.039 1.532 0.152 0.055 0.081 -mile kilometer 0.250 2.251 0.800 0.546 6.061 0.579 0.102 0.563 -skin eye 0.167 1.846 0.667 0.362 4.402 0.490 0.098 0.276 -Japanese American 0.250 2.251 0.632 0.549 6.424 0.695 0.156 0.565 -century year 0.333 2.539 0.833 0.659 3.800 0.484 0.110 0.539 -announcement news 0.200 2.028 0.667 0.425 3.848 0.490 0.111 0.371 -doctor personnel 0.083 1.153 0.267 0.060 0.596 0.076 0.066 0.094 -Harvard Yale 0.333 2.539 0.917 0.670 9.922 0.860 0.237 0.821 -hospital infrastructure 0.083 1.153 0.267 0.060 0.596 0.061 0.052 0.094 -life death 0.333 2.539 0.857 0.667 7.411 0.888 0.349 0.723 -travel activity 0.167 1.846 0.667 0.362 2.332 0.500 0.177 0.252 -type kind 0.500 2.944 0.947 0.819 6.421 0.896 0.402 0.807 -street place 0.167 1.846 0.615 0.348 3.140 0.366 0.084 0.287 -street avenue 0.500 2.944 0.947 0.819 8.670 0.913 0.377 0.874 -street block 0.167 1.846 0.667 0.362 2.855 0.306 0.087 0.251 -cell phone 0.333 2.539 0.900 0.670 8.723 0.959 0.575 0.778 -dividend payment 0.333 2.539 0.889 0.670 6.737 0.726 0.164 0.692 -calculation computation 1.000 3.638 1.000 1.000 7.760 1.000 1.000 1.000 -profit loss 0.167 1.846 0.706 0.366 6.161 0.770 0.213 0.442 -dollar yen 0.250 2.251 0.800 0.546 6.875 0.610 0.102 0.607 -dollar buck 1.000 3.638 1.000 1.000 9.284 1.000 1.000 1.000 -phone equipment 0.333 2.539 0.875 0.669 5.268 0.753 0.224 0.618 -liquid water 0.500 2.944 0.933 0.818 8.115 0.994 0.916 0.859 -marathon sprint 0.091 1.240 0.500 0.133 2.332 0.190 0.048 0.144 -seafood food 0.500 2.944 0.909 0.805 6.109 0.790 0.235 0.796 -seafood lobster 0.333 2.539 0.857 0.667 9.360 0.809 0.185 0.801 -lobster food 0.250 2.251 0.769 0.540 6.109 0.615 0.115 0.566 -lobster wine 0.091 1.240 0.375 0.113 3.337 0.297 0.059 0.174 -championship tournament 0.333 2.539 0.857 0.667 6.609 0.633 0.115 0.686 -man woman 0.333 2.539 0.706 0.670 4.910 0.787 0.273 0.599 -man governor 0.200 2.028 0.667 0.449 3.258 0.402 0.093 0.296 -murder manslaughter 0.333 2.539 0.917 0.670 8.560 0.785 0.176 0.772 -opera performance 0.100 1.335 0.308 0.089 0.802 0.084 0.057 0.117 -Mexico Brazil 0.200 2.028 0.800 0.449 6.134 0.495 0.074 0.496 -glass metal 0.167 1.846 0.545 0.307 3.337 0.454 0.111 0.296 -aluminum metal 0.500 2.944 0.933 0.818 5.442 0.667 0.155 0.771 -rock jazz 0.333 2.539 0.875 0.669 8.409 0.864 0.275 0.766 -museum theater 0.143 1.692 0.625 0.296 2.306 0.248 0.067 0.218 -shower thunderstorm 0.143 1.692 0.700 0.301 6.561 0.000 0.000 0.419 -monk oracle 0.125 1.558 0.571 0.246 2.334 0.226 0.059 0.194 -cup food 0.167 1.846 0.667 0.362 5.279 0.672 0.163 0.394 -journal association 0.143 1.692 0.400 0.162 0.802 0.081 0.059 0.160 -street children 0.100 1.335 0.471 0.157 1.532 0.198 0.074 0.135 -car flight 0.125 1.558 0.588 0.243 2.306 0.256 0.070 0.193 -space chemistry 0.167 1.846 0.444 0.198 0.802 0.089 0.057 0.186 -situation conclusion 0.143 1.692 0.625 0.296 2.332 0.346 0.102 0.219 -word similarity 0.111 1.440 0.455 0.108 3.292 0.335 0.071 0.148 -peace plan 0.100 1.335 0.308 0.089 0.596 0.076 0.065 0.113 -consumer energy 0.100 1.335 0.308 0.089 0.802 0.114 0.074 0.117 -ministry culture 0.125 1.558 0.533 0.233 3.336 0.314 0.064 0.231 -smart student 0.071 0.999 0.133 0.000 0.000 0.000 0.052 0.071 -investigation effort 0.250 2.251 0.824 0.548 4.806 0.693 0.190 0.493 -image surface 0.200 2.028 0.714 0.442 3.292 0.378 0.099 0.295 -life term 0.333 2.539 0.857 0.667 6.494 0.905 0.423 0.680 -start match 0.143 1.692 0.571 0.285 2.691 0.307 0.078 0.233 -computer news 0.091 1.240 0.167 0.000 0.000 0.000 0.057 0.091 -board recommendation 0.083 1.153 0.267 0.060 0.596 0.068 0.057 0.094 -lad brother 0.200 2.028 0.667 0.449 2.334 0.255 0.068 0.296 -observation architecture 0.167 1.846 0.706 0.366 3.144 0.295 0.067 0.287 -coast hill 0.200 2.028 0.667 0.425 5.885 0.599 0.113 0.482 -deployment departure 0.200 2.028 0.714 0.442 2.332 0.212 0.054 0.296 -benchmark index 0.250 2.251 0.769 0.540 6.110 0.514 0.080 0.566 -attempt peace 0.100 1.335 0.308 0.089 0.596 0.077 0.065 0.113 -consumer confidence 0.083 1.153 0.154 0.000 0.000 0.000 0.057 0.083 -start year 0.167 1.846 0.545 0.307 2.691 0.385 0.104 0.267 -focus life 0.143 1.692 0.533 0.251 3.140 0.371 0.086 0.224 -development issue 0.250 2.251 0.750 0.458 4.089 0.567 0.138 0.454 -theater history 0.143 1.692 0.500 0.251 1.593 0.182 0.065 0.192 -situation isolation 0.250 2.251 0.727 0.520 3.140 0.403 0.097 0.402 -profit warning 0.091 1.240 0.286 0.073 0.596 0.074 0.063 0.103 -media trading 0.143 1.692 0.625 0.296 2.332 0.231 0.060 0.219 -chance credibility 0.167 1.846 0.500 0.251 2.298 0.268 0.074 0.218 -precedent information 0.333 2.539 0.833 0.659 5.081 0.606 0.131 0.608 -architecture century 0.100 1.335 0.308 0.089 0.596 0.063 0.054 0.113 -population development 0.143 1.692 0.727 0.301 5.654 0.642 0.137 0.370 -peace atmosphere 0.200 2.028 0.714 0.442 4.235 0.501 0.106 0.391 -morality marriage 0.167 1.846 0.545 0.307 2.298 0.306 0.088 0.250 -minority peace 0.167 1.846 0.615 0.348 3.140 0.303 0.065 0.287 -atmosphere landscape 0.111 1.440 0.500 0.191 3.012 0.299 0.066 0.197 -report gain 0.125 1.558 0.364 0.132 0.596 0.084 0.071 0.140 -music project 0.250 2.251 0.800 0.546 3.144 0.432 0.108 0.402 -seven series 0.111 1.440 0.375 0.113 3.037 0.316 0.071 0.165 -experience music 0.167 1.846 0.615 0.348 2.645 0.329 0.086 0.240 -school center 0.333 2.539 0.875 0.669 5.715 0.669 0.150 0.642 -five month 0.125 1.558 0.462 0.206 2.691 0.374 0.100 0.207 -announcement production 0.143 1.692 0.500 0.251 2.620 0.301 0.076 0.230 -morality importance 0.200 2.028 0.667 0.425 3.965 0.583 0.150 0.377 -money operation 0.111 1.440 0.333 0.108 0.596 0.096 0.082 0.125 -delay news 0.125 1.558 0.533 0.233 2.036 0.213 0.062 0.184 -governor interview 0.071 0.999 0.133 0.000 0.000 0.000 0.050 0.071 -practice institution 0.333 2.539 0.833 0.659 7.355 0.871 0.315 0.721 -century nation 0.125 1.558 0.364 0.132 0.596 0.083 0.070 0.140 -coast forest 0.167 1.846 0.545 0.307 1.290 0.131 0.055 0.211 -shore woodland 0.200 2.028 0.600 0.375 1.290 0.136 0.057 0.250 -drink car 0.091 1.240 0.286 0.073 0.802 0.112 0.073 0.107 -president medal 0.077 1.073 0.235 0.040 0.596 0.063 0.054 0.081 -prejudice recognition 0.167 1.846 0.667 0.362 6.039 0.555 0.094 0.435 -viewer serial 0.091 1.240 0.500 0.133 2.306 0.212 0.061 0.143 -peace insurance 0.250 2.251 0.824 0.548 8.459 0.842 0.256 0.688 -Mars water 0.111 1.440 0.500 0.191 1.532 0.155 0.066 0.150 -media gain 0.143 1.692 0.500 0.251 3.037 0.369 0.088 0.220 -precedent cognition 0.250 2.251 0.727 0.520 2.645 0.369 0.100 0.376 -announcement effort 0.100 1.335 0.308 0.089 0.596 0.085 0.072 0.113 -line insurance 0.200 2.028 0.714 0.442 5.176 0.559 0.121 0.442 -crane implement 0.200 2.028 0.750 0.447 3.258 0.359 0.079 0.341 -drink mother 0.167 1.846 0.615 0.348 4.615 0.595 0.137 0.359 -opera industry 0.091 1.240 0.286 0.073 0.596 0.065 0.055 0.103 -volunteer motto 0.083 1.153 0.154 0.000 0.000 0.000 0.047 0.083 -listing proximity 0.077 1.073 0.316 0.062 1.593 0.135 0.057 0.099 -precedent collection 0.333 2.539 0.800 0.635 5.053 0.608 0.133 0.607 -cup article 0.200 2.028 0.750 0.447 7.332 0.830 0.250 0.562 -sign recess 0.200 2.028 0.750 0.447 3.979 0.438 0.089 0.378 -problem airport 0.071 0.999 0.133 0.000 0.000 0.000 0.056 0.071 -reason hypertension 0.111 1.440 0.556 0.199 4.235 0.385 0.069 0.243 -direction combination 0.143 1.692 0.667 0.300 3.144 0.334 0.088 0.252 -Wednesday news 0.083 1.153 0.267 0.060 0.596 0.067 0.056 0.094 -glass magician 0.125 1.558 0.471 0.206 2.283 0.214 0.056 0.192 -cemetery woodland 0.111 1.440 0.429 0.168 1.290 0.123 0.052 0.143 -possibility girl 0.091 1.240 0.167 0.000 0.000 0.000 0.068 0.091 -cup substance 0.143 1.692 0.571 0.285 4.615 0.614 0.147 0.318 -forest graveyard 0.111 1.440 0.429 0.168 1.290 0.123 0.052 0.143 -stock egg 0.143 1.692 0.625 0.296 5.279 0.545 0.102 0.351 -month hotel 0.083 1.153 0.154 0.000 0.000 0.000 0.063 0.083 -energy secretary 0.091 1.240 0.286 0.073 0.802 0.102 0.066 0.107 -precedent group 0.250 2.251 0.667 0.458 2.855 0.396 0.103 0.387 -production hike 0.125 1.558 0.588 0.243 2.332 0.232 0.064 0.194 -stock phone 0.143 1.692 0.700 0.301 4.076 0.414 0.093 0.293 -holy sex 0.100 1.335 0.308 0.089 0.802 0.091 0.059 0.117 -stock CD 0.167 1.846 0.737 0.367 4.076 0.372 0.068 0.332 -drink ear 0.125 1.558 0.462 0.206 3.968 0.395 0.076 0.257 -delay racism 0.143 1.692 0.625 0.296 2.332 0.211 0.054 0.219 -stock life 0.167 1.846 0.615 0.348 3.140 0.368 0.085 0.224 -stock jaguar 0.167 1.846 0.815 0.368 5.414 0.494 0.083 0.401 -monk slave 0.200 2.028 0.667 0.449 2.334 0.254 0.068 0.296 -lad wizard 0.200 2.028 0.667 0.449 2.334 0.255 0.068 0.296 -sugar approach 0.091 1.240 0.286 0.073 0.802 0.087 0.056 0.107 -rooster voyage 0.042 0.460 0.080 0.000 0.000 0.000 0.042 0.042 -noon string 0.083 1.153 0.267 0.060 0.596 0.066 0.056 0.094 -chord smile 0.091 1.240 0.375 0.113 2.620 0.246 0.059 0.152 -professor cucumber 0.077 1.073 0.500 0.090 2.224 0.065 0.042 0.120 -king cabbage 0.100 1.335 0.571 0.164 3.337 0.305 0.062 0.154 diff --git a/sematch/eval/word_similarity/results_original/noun_ws353.txt b/sematch/eval/word_similarity/results_original/noun_ws353.txt deleted file mode 100644 index 24af7d1..0000000 --- a/sematch/eval/word_similarity/results_original/noun_ws353.txt +++ /dev/null @@ -1,349 +0,0 @@ -word1 word2 path lch wup li res lin jcn wpath -love sex 0.500 2.944 0.923 0.815 6.690 0.852 0.300 0.817 -tiger cat 0.500 2.944 0.966 0.819 9.841 0.894 0.301 0.900 -tiger tiger 1.000 3.638 1.000 1.000 12.268 1.000 1.000 1.000 -book paper 0.333 2.539 0.875 0.669 5.455 0.674 0.159 0.628 -computer keyboard 0.250 2.251 0.824 0.548 4.076 0.374 0.068 0.453 -computer internet 0.125 1.558 0.632 0.245 3.258 0.330 0.070 0.228 -plane car 0.143 1.692 0.727 0.301 5.922 0.719 0.178 0.385 -train car 0.167 1.846 0.737 0.367 5.769 0.679 0.155 0.420 -telephone communication 0.091 1.240 0.167 0.000 0.000 0.000 0.081 0.091 -television radio 0.333 2.539 0.909 0.670 9.123 0.920 0.386 0.793 -media radio 0.250 2.251 0.824 0.548 7.021 0.832 0.261 0.615 -drug abuse 0.100 1.335 0.182 0.000 0.000 0.000 0.063 0.100 -bread butter 0.250 2.251 0.750 0.546 6.880 0.711 0.152 0.607 -cucumber potato 0.250 2.251 0.824 0.548 9.442 0.659 0.103 0.686 -doctor nurse 0.250 2.251 0.870 0.549 7.930 0.835 0.242 0.662 -professor doctor 0.143 1.692 0.545 0.301 6.394 0.704 0.157 0.410 -student professor 0.125 1.558 0.571 0.246 2.334 0.276 0.076 0.194 -smart student 0.071 0.999 0.133 0.000 0.000 0.000 0.052 0.071 -smart stupid 0.071 0.999 0.133 0.000 0.000 0.000 0.042 0.071 -company stock 0.167 1.846 0.615 0.348 3.336 0.483 0.123 0.296 -stock market 0.143 1.692 0.556 0.251 3.336 0.455 0.111 0.240 -stock phone 0.143 1.692 0.700 0.301 4.076 0.414 0.093 0.293 -stock CD 0.167 1.846 0.737 0.367 4.076 0.372 0.068 0.332 -stock jaguar 0.167 1.846 0.815 0.368 5.414 0.494 0.083 0.401 -stock egg 0.143 1.692 0.625 0.296 5.279 0.545 0.102 0.351 -fertility egg 0.083 1.153 0.154 0.000 0.000 0.000 0.045 0.083 -stock life 0.167 1.846 0.615 0.348 3.140 0.368 0.085 0.224 -book library 0.333 2.539 0.800 0.635 5.053 0.537 0.103 0.607 -bank money 0.143 1.692 0.571 0.285 4.470 0.483 0.095 0.311 -wood forest 1.000 3.638 1.000 0.995 9.002 1.000 1.000 1.000 -money cash 0.333 2.539 0.875 0.669 6.445 0.789 0.230 0.678 -professor cucumber 0.077 1.073 0.500 0.090 2.224 0.065 0.042 0.120 -king cabbage 0.100 1.335 0.571 0.164 3.337 0.305 0.062 0.154 -king queen 1.000 3.638 1.000 1.000 9.568 1.000 1.000 1.000 -king rook 0.333 2.539 0.909 0.670 8.505 0.772 0.166 0.769 -bishop rabbi 0.200 2.028 0.545 0.449 6.962 0.666 0.125 0.542 -Jerusalem Israel 0.143 1.692 0.700 0.301 4.389 0.367 0.062 0.307 -Jerusalem Palestinian 0.063 0.865 0.286 0.042 1.290 0.000 0.000 0.082 -holy sex 0.100 1.335 0.308 0.089 0.802 0.091 0.059 0.117 -fuck sex 0.333 2.539 0.857 0.667 6.690 0.770 0.200 0.690 -football soccer 0.500 2.944 0.880 0.819 11.064 0.906 0.303 0.922 -football basketball 0.333 2.539 0.900 0.670 8.716 0.754 0.149 0.771 -football tennis 0.167 1.846 0.783 0.368 8.716 0.770 0.161 0.583 -tennis racket 0.111 1.440 0.600 0.201 3.144 0.262 0.054 0.201 -Arafat peace 0.077 1.073 0.143 0.000 0.000 0.000 0.000 0.077 -Arafat terror 0.167 1.846 0.632 0.367 2.334 0.000 0.000 0.252 -Arafat Jackson 0.250 2.251 0.571 0.549 6.737 0.000 0.000 0.600 -law lawyer 0.091 1.240 0.167 0.000 0.000 0.000 0.058 0.091 -movie star 0.125 1.558 0.533 0.233 1.593 0.189 0.071 0.167 -movie popcorn 0.071 0.999 0.364 0.058 1.532 0.000 0.000 0.091 -movie critic 0.091 1.240 0.444 0.128 1.532 0.180 0.067 0.123 -movie theater 0.143 1.692 0.625 0.296 2.306 0.293 0.083 0.218 -physics proton 0.067 0.930 0.222 0.033 0.802 0.066 0.042 0.079 -physics chemistry 0.333 2.539 0.900 0.670 8.716 0.818 0.205 0.778 -space chemistry 0.167 1.846 0.444 0.198 0.802 0.089 0.057 0.186 -alcohol chemistry 0.200 2.028 0.571 0.442 3.886 0.399 0.079 0.373 -vodka gin 0.333 2.539 0.909 0.670 8.856 0.000 0.000 0.783 -vodka brandy 0.333 2.539 0.909 0.670 8.856 0.000 0.000 0.783 -drink car 0.091 1.240 0.286 0.073 0.802 0.112 0.073 0.107 -drink ear 0.125 1.558 0.462 0.206 3.968 0.395 0.076 0.257 -drink mouth 0.143 1.692 0.500 0.168 3.968 0.426 0.086 0.233 -baby mother 0.200 2.028 0.632 0.449 6.257 0.684 0.147 0.502 -drink mother 0.167 1.846 0.615 0.348 4.615 0.595 0.137 0.359 -car automobile 1.000 3.638 1.000 1.000 7.591 1.000 1.000 1.000 -gem jewel 1.000 3.638 1.000 1.000 12.068 1.000 1.000 1.000 -journey voyage 0.500 2.944 0.952 0.819 6.826 0.778 0.204 0.821 -boy lad 0.500 2.944 0.947 0.819 8.399 0.831 0.226 0.867 -coast shore 0.500 2.944 0.909 0.805 9.416 0.963 0.582 0.891 -asylum madhouse 0.500 2.944 0.952 0.819 9.475 0.856 0.238 0.892 -magician wizard 1.000 3.638 0.667 1.000 11.981 1.000 1.000 1.000 -midday noon 1.000 3.638 1.000 1.000 11.064 1.000 1.000 1.000 -furnace stove 0.100 1.335 0.526 0.163 2.306 0.228 0.060 0.157 -food fruit 0.100 1.335 0.400 0.138 1.593 0.161 0.084 0.137 -bird cock 0.500 2.944 0.952 0.819 7.678 0.774 0.182 0.847 -bird crane 0.250 2.251 0.870 0.549 7.678 0.748 0.162 0.649 -tool implement 0.500 2.944 0.933 0.818 5.877 0.947 0.604 0.788 -brother monk 0.500 2.944 0.952 0.819 9.262 0.986 0.797 0.888 -crane implement 0.200 2.028 0.750 0.447 3.258 0.359 0.079 0.341 -lad brother 0.200 2.028 0.667 0.449 2.334 0.255 0.068 0.296 -journey car 0.056 0.747 0.105 0.000 0.000 0.000 0.065 0.056 -monk oracle 0.125 1.558 0.571 0.246 2.334 0.226 0.059 0.194 -cemetery woodland 0.111 1.440 0.429 0.168 1.290 0.123 0.052 0.143 -food rooster 0.063 0.865 0.211 0.027 0.802 0.092 0.059 0.074 -coast hill 0.200 2.028 0.667 0.425 5.885 0.599 0.113 0.482 -forest graveyard 0.111 1.440 0.429 0.168 1.290 0.123 0.052 0.143 -shore woodland 0.200 2.028 0.600 0.375 1.290 0.136 0.057 0.250 -monk slave 0.200 2.028 0.667 0.449 2.334 0.254 0.068 0.296 -coast forest 0.167 1.846 0.545 0.307 1.290 0.131 0.055 0.211 -lad wizard 0.200 2.028 0.667 0.449 2.334 0.255 0.068 0.296 -chord smile 0.091 1.240 0.375 0.113 2.620 0.246 0.059 0.152 -glass magician 0.125 1.558 0.471 0.206 2.283 0.214 0.056 0.192 -noon string 0.083 1.153 0.267 0.060 0.596 0.066 0.056 0.094 -rooster voyage 0.042 0.460 0.080 0.000 0.000 0.000 0.042 0.042 -money dollar 0.200 2.028 0.778 0.449 6.445 0.722 0.172 0.513 -money cash 0.333 2.539 0.875 0.669 6.445 0.789 0.230 0.678 -money currency 0.500 2.944 0.933 0.818 6.445 0.864 0.336 0.808 -money wealth 0.500 2.944 0.923 0.815 8.660 0.996 0.936 0.874 -money property 0.333 2.539 0.833 0.659 6.897 0.883 0.353 0.700 -money possession 0.250 2.251 0.727 0.520 4.470 0.677 0.190 0.475 -money bank 0.143 1.692 0.571 0.285 4.470 0.483 0.095 0.311 -money deposit 0.333 2.539 0.875 0.669 8.042 0.812 0.212 0.751 -money withdrawal 0.091 1.240 0.286 0.073 0.596 0.067 0.057 0.103 -money laundering 0.077 1.073 0.250 0.049 0.596 0.058 0.049 0.087 -money operation 0.111 1.440 0.333 0.108 0.596 0.096 0.082 0.125 -tiger jaguar 0.333 2.539 0.933 0.670 9.841 0.797 0.167 0.818 -tiger feline 0.333 2.539 0.929 0.670 8.663 0.832 0.222 0.776 -tiger carnivore 0.250 2.251 0.889 0.549 7.912 0.788 0.190 0.661 -tiger mammal 0.167 1.846 0.800 0.368 5.406 0.615 0.129 0.401 -tiger animal 0.250 2.251 0.800 0.546 4.946 0.578 0.122 0.354 -tiger organism 0.333 2.539 0.857 0.667 2.224 0.309 0.091 0.451 -tiger fauna 0.250 2.251 0.800 0.546 4.946 0.578 0.122 0.354 -tiger zoo 0.125 1.558 0.533 0.233 1.532 0.126 0.045 0.167 -psychology psychiatry 0.143 1.692 0.727 0.301 7.187 0.655 0.117 0.453 -psychology anxiety 0.077 1.073 0.250 0.049 0.596 0.068 0.057 0.087 -psychology fear 0.077 1.073 0.250 0.049 0.596 0.066 0.056 0.087 -psychology depression 0.083 1.153 0.353 0.092 1.593 0.153 0.058 0.115 -psychology clinic 0.071 0.999 0.235 0.040 0.596 0.053 0.045 0.081 -psychology doctor 0.077 1.073 0.333 0.076 1.593 0.165 0.059 0.106 -psychology Freud 0.050 0.642 0.095 0.000 0.000 0.000 0.044 0.050 -psychology mind 0.143 1.692 0.588 0.285 3.292 0.374 0.083 0.231 -psychology health 0.083 1.153 0.267 0.060 0.596 0.060 0.051 0.094 -psychology science 0.500 2.944 0.941 0.818 7.187 0.847 0.279 0.833 -psychology discipline 0.333 2.539 0.875 0.669 5.589 0.728 0.193 0.635 -psychology cognition 0.167 1.846 0.615 0.348 2.645 0.426 0.123 0.265 -planet star 0.333 2.539 0.857 0.667 6.932 0.874 0.334 0.701 -planet constellation 0.250 2.251 0.769 0.540 4.940 0.592 0.128 0.501 -planet moon 0.250 2.251 0.800 0.546 6.932 0.781 0.204 0.610 -planet sun 0.250 2.251 0.800 0.546 6.932 0.810 0.236 0.610 -planet galaxy 0.125 1.558 0.632 0.245 2.224 0.197 0.057 0.190 -planet space 0.143 1.692 0.533 0.251 1.532 0.191 0.084 0.182 -planet astronomer 0.167 1.846 0.632 0.367 2.334 0.242 0.070 0.252 -precedent example 0.500 2.944 0.923 0.815 8.077 0.817 0.217 0.858 -precedent information 0.333 2.539 0.833 0.659 5.081 0.606 0.131 0.608 -precedent cognition 0.250 2.251 0.727 0.520 2.645 0.369 0.100 0.376 -precedent law 0.500 2.944 0.909 0.805 8.372 0.839 0.238 0.866 -precedent collection 0.333 2.539 0.800 0.635 5.053 0.608 0.133 0.607 -precedent group 0.250 2.251 0.667 0.458 2.855 0.396 0.103 0.387 -precedent antecedent 0.167 1.846 0.615 0.348 3.848 0.305 0.054 0.321 -cup coffee 0.167 1.846 0.762 0.368 6.693 0.651 0.122 0.471 -cup tableware 0.333 2.539 0.889 0.670 7.490 0.840 0.260 0.727 -cup article 0.200 2.028 0.750 0.447 7.332 0.830 0.250 0.562 -cup artifact 0.250 2.251 0.769 0.540 2.306 0.365 0.111 0.358 -cup object 0.167 1.846 0.545 0.307 2.620 0.261 0.100 0.211 -cup entity 0.167 1.846 0.286 0.000 0.000 0.000 0.088 0.167 -cup drink 0.200 2.028 0.800 0.449 6.693 0.782 0.211 0.527 -cup food 0.167 1.846 0.667 0.362 5.279 0.672 0.163 0.394 -cup substance 0.143 1.692 0.571 0.285 4.615 0.614 0.147 0.318 -cup liquid 0.167 1.846 0.737 0.367 6.444 0.764 0.201 0.457 -jaguar cat 0.500 2.944 0.966 0.819 9.841 0.880 0.272 0.900 -jaguar car 0.059 0.804 0.333 0.039 1.532 0.152 0.055 0.081 -energy secretary 0.091 1.240 0.286 0.073 0.802 0.102 0.066 0.107 -secretary senate 0.071 0.999 0.133 0.000 0.000 0.000 0.051 0.071 -energy laboratory 0.100 1.335 0.308 0.089 0.802 0.093 0.060 0.117 -computer laboratory 0.111 1.440 0.429 0.168 1.290 0.134 0.056 0.143 -weapon secret 0.091 1.240 0.286 0.073 0.596 0.067 0.063 0.103 -FBI fingerprint 0.077 1.073 0.250 0.049 0.596 0.051 0.043 0.087 -FBI investigation 0.067 0.930 0.222 0.033 0.596 0.064 0.054 0.075 -investigation effort 0.250 2.251 0.824 0.548 4.806 0.693 0.190 0.493 -Mars water 0.111 1.440 0.500 0.191 1.532 0.155 0.066 0.150 -Mars scientist 0.100 1.335 0.471 0.157 1.532 0.176 0.066 0.135 -news report 0.500 2.944 0.923 0.815 7.743 0.989 0.849 0.849 -canyon landscape 0.083 1.153 0.353 0.092 1.290 0.114 0.047 0.108 -image surface 0.200 2.028 0.714 0.442 3.292 0.378 0.099 0.295 -discovery space 0.143 1.692 0.400 0.162 0.596 0.089 0.076 0.160 -water seepage 0.083 1.153 0.250 0.049 0.596 0.056 0.050 0.087 -sign recess 0.200 2.028 0.750 0.447 3.979 0.438 0.089 0.378 -Wednesday news 0.083 1.153 0.267 0.060 0.596 0.067 0.056 0.094 -mile kilometer 0.250 2.251 0.800 0.546 6.061 0.579 0.102 0.563 -computer news 0.091 1.240 0.167 0.000 0.000 0.000 0.057 0.091 -territory surface 0.167 1.846 0.615 0.348 3.292 0.557 0.173 0.281 -atmosphere landscape 0.111 1.440 0.500 0.191 3.012 0.299 0.066 0.197 -president medal 0.077 1.073 0.235 0.040 0.596 0.063 0.054 0.081 -war troops 0.100 1.335 0.308 0.089 0.596 0.071 0.060 0.113 -record number 0.500 2.944 0.909 0.805 5.819 0.796 0.251 0.726 -skin eye 0.167 1.846 0.667 0.362 4.402 0.490 0.098 0.276 -Japanese American 0.250 2.251 0.632 0.549 6.424 0.695 0.156 0.565 -theater history 0.143 1.692 0.500 0.251 1.593 0.182 0.065 0.192 -volunteer motto 0.083 1.153 0.154 0.000 0.000 0.000 0.047 0.083 -prejudice recognition 0.167 1.846 0.667 0.362 6.039 0.555 0.094 0.435 -decoration valor 0.083 1.153 0.267 0.060 0.596 0.052 0.050 0.094 -century year 0.333 2.539 0.833 0.659 3.800 0.484 0.110 0.539 -century nation 0.125 1.558 0.364 0.132 0.596 0.083 0.070 0.140 -delay racism 0.143 1.692 0.625 0.296 2.332 0.211 0.054 0.219 -delay news 0.125 1.558 0.533 0.233 2.036 0.213 0.062 0.184 -minister party 0.167 1.846 0.632 0.367 2.334 0.259 0.070 0.252 -peace plan 0.100 1.335 0.308 0.089 0.596 0.076 0.065 0.113 -minority peace 0.167 1.846 0.615 0.348 3.140 0.303 0.065 0.287 -attempt peace 0.100 1.335 0.308 0.089 0.596 0.077 0.065 0.113 -government crisis 0.143 1.692 0.571 0.285 2.036 0.236 0.070 0.208 -deployment departure 0.200 2.028 0.714 0.442 2.332 0.212 0.054 0.296 -deployment withdrawal 0.200 2.028 0.778 0.449 8.772 0.650 0.096 0.639 -energy crisis 0.143 1.692 0.625 0.296 4.235 0.443 0.086 0.300 -announcement news 0.200 2.028 0.667 0.425 3.848 0.490 0.111 0.371 -announcement effort 0.100 1.335 0.308 0.089 0.596 0.085 0.072 0.113 -stroke hospital 0.091 1.240 0.400 0.086 1.532 0.155 0.063 0.105 -disability death 0.125 1.558 0.533 0.233 3.140 0.336 0.074 0.224 -victim emergency 0.100 1.335 0.444 0.128 1.532 0.159 0.058 0.123 -treatment recovery 0.250 2.251 0.769 0.540 2.332 0.279 0.076 0.359 -journal association 0.143 1.692 0.400 0.162 0.802 0.081 0.059 0.160 -doctor personnel 0.083 1.153 0.267 0.060 0.596 0.076 0.066 0.094 -doctor liability 0.083 1.153 0.267 0.060 0.596 0.065 0.056 0.094 -liability insurance 0.143 1.692 0.625 0.296 4.235 0.360 0.062 0.300 -school center 0.333 2.539 0.875 0.669 5.715 0.669 0.150 0.642 -reason hypertension 0.111 1.440 0.556 0.199 4.235 0.385 0.069 0.243 -reason criterion 0.125 1.558 0.533 0.233 2.645 0.316 0.080 0.205 -hundred percent 0.077 1.073 0.250 0.049 0.596 0.068 0.058 0.087 -Harvard Yale 0.333 2.539 0.917 0.670 9.922 0.860 0.237 0.821 -hospital infrastructure 0.083 1.153 0.267 0.060 0.596 0.061 0.052 0.094 -death row 0.125 1.558 0.500 0.206 2.332 0.267 0.076 0.193 -death inmate 0.091 1.240 0.286 0.073 0.802 0.080 0.051 0.107 -lawyer evidence 0.091 1.240 0.167 0.000 0.000 0.000 0.066 0.091 -life death 0.333 2.539 0.857 0.667 7.411 0.888 0.349 0.723 -life term 0.333 2.539 0.857 0.667 6.494 0.905 0.423 0.680 -word similarity 0.111 1.440 0.455 0.108 3.292 0.335 0.071 0.148 -board recommendation 0.083 1.153 0.267 0.060 0.596 0.068 0.057 0.094 -governor interview 0.071 0.999 0.133 0.000 0.000 0.000 0.050 0.071 -OPEC country 0.143 1.692 0.625 0.296 3.907 0.000 0.000 0.285 -peace atmosphere 0.200 2.028 0.714 0.442 4.235 0.501 0.106 0.391 -peace insurance 0.250 2.251 0.824 0.548 8.459 0.842 0.256 0.688 -territory kilometer 0.077 1.073 0.250 0.049 0.596 0.052 0.058 0.087 -travel activity 0.167 1.846 0.667 0.362 2.332 0.500 0.177 0.252 -competition price 0.125 1.558 0.545 0.206 3.037 0.321 0.072 0.220 -consumer confidence 0.083 1.153 0.154 0.000 0.000 0.000 0.057 0.083 -consumer energy 0.100 1.335 0.308 0.089 0.802 0.114 0.074 0.117 -problem airport 0.071 0.999 0.133 0.000 0.000 0.000 0.056 0.071 -car flight 0.125 1.558 0.588 0.243 2.306 0.256 0.070 0.193 -credit card 0.167 1.846 0.615 0.348 4.004 0.429 0.110 0.321 -credit information 0.250 2.251 0.727 0.520 4.934 0.551 0.125 0.440 -hotel reservation 0.091 1.240 0.375 0.113 1.290 0.141 0.060 0.118 -grocery money 0.071 0.999 0.133 0.000 0.000 0.000 0.054 0.071 -registration arrangement 0.200 2.028 0.667 0.375 3.144 0.359 0.084 0.321 -arrangement accommodation 0.143 1.692 0.667 0.300 4.385 0.410 0.093 0.252 -month hotel 0.083 1.153 0.154 0.000 0.000 0.000 0.063 0.083 -type kind 0.500 2.944 0.947 0.819 6.421 0.896 0.402 0.807 -arrival hotel 0.100 1.335 0.471 0.157 1.532 0.160 0.059 0.135 -bed closet 0.250 2.251 0.842 0.549 6.616 0.739 0.176 0.593 -closet clothes 0.125 1.558 0.588 0.243 2.306 0.245 0.066 0.193 -situation conclusion 0.143 1.692 0.625 0.296 2.332 0.346 0.102 0.219 -situation isolation 0.250 2.251 0.727 0.520 3.140 0.403 0.097 0.402 -impartiality interest 0.111 1.440 0.400 0.138 1.593 0.156 0.055 0.137 -direction combination 0.143 1.692 0.667 0.300 3.144 0.334 0.088 0.252 -street place 0.167 1.846 0.615 0.348 3.140 0.366 0.084 0.287 -street avenue 0.500 2.944 0.947 0.819 8.670 0.913 0.377 0.874 -street block 0.167 1.846 0.667 0.362 2.855 0.306 0.087 0.251 -street children 0.100 1.335 0.471 0.157 1.532 0.198 0.074 0.135 -listing proximity 0.077 1.073 0.316 0.062 1.593 0.135 0.057 0.099 -listing category 0.111 1.440 0.375 0.113 1.593 0.189 0.076 0.125 -cell phone 0.333 2.539 0.900 0.670 8.723 0.959 0.575 0.778 -production hike 0.125 1.558 0.588 0.243 2.332 0.232 0.064 0.194 -benchmark index 0.250 2.251 0.769 0.540 6.110 0.514 0.080 0.566 -media trading 0.143 1.692 0.625 0.296 2.332 0.231 0.060 0.219 -media gain 0.143 1.692 0.500 0.251 3.037 0.369 0.088 0.220 -dividend payment 0.333 2.539 0.889 0.670 6.737 0.726 0.164 0.692 -dividend calculation 0.091 1.240 0.286 0.073 0.596 0.062 0.052 0.103 -calculation computation 1.000 3.638 1.000 1.000 7.760 1.000 1.000 1.000 -currency market 0.100 1.335 0.308 0.089 0.596 0.080 0.068 0.113 -OPEC oil 0.071 0.999 0.235 0.040 0.596 0.000 0.000 0.081 -oil stock 0.111 1.440 0.571 0.199 5.118 0.524 0.097 0.259 -announcement production 0.143 1.692 0.500 0.251 2.620 0.301 0.076 0.230 -announcement warning 0.167 1.846 0.615 0.348 3.848 0.457 0.099 0.321 -profit warning 0.091 1.240 0.286 0.073 0.596 0.074 0.063 0.103 -profit loss 0.167 1.846 0.706 0.366 6.161 0.770 0.213 0.442 -dollar yen 0.250 2.251 0.800 0.546 6.875 0.610 0.102 0.607 -dollar buck 1.000 3.638 1.000 1.000 9.284 1.000 1.000 1.000 -dollar profit 0.100 1.335 0.308 0.089 0.596 0.072 0.061 0.113 -dollar loss 0.111 1.440 0.333 0.108 0.596 0.069 0.059 0.125 -computer software 0.077 1.073 0.143 0.000 0.000 0.000 0.060 0.077 -network hardware 0.250 2.251 0.800 0.546 3.258 0.293 0.060 0.408 -phone equipment 0.333 2.539 0.875 0.669 5.268 0.753 0.224 0.618 -equipment maker 0.111 1.440 0.500 0.191 1.532 0.234 0.091 0.150 -luxury car 0.067 0.930 0.125 0.000 0.000 0.000 0.050 0.067 -five month 0.125 1.558 0.462 0.206 2.691 0.374 0.100 0.207 -report gain 0.125 1.558 0.364 0.132 0.596 0.084 0.071 0.140 -liquid water 0.500 2.944 0.933 0.818 8.115 0.994 0.916 0.859 -baseball season 0.067 0.930 0.222 0.033 0.596 0.069 0.059 0.075 -game victory 0.143 1.692 0.571 0.285 2.036 0.264 0.081 0.208 -game team 0.100 1.335 0.308 0.089 0.596 0.084 0.071 0.113 -marathon sprint 0.091 1.240 0.500 0.133 2.332 0.190 0.048 0.144 -game series 0.333 2.539 0.857 0.667 6.609 0.699 0.150 0.686 -game defeat 0.143 1.692 0.571 0.285 2.036 0.235 0.070 0.208 -seven series 0.111 1.440 0.375 0.113 3.037 0.316 0.071 0.165 -seafood sea 0.125 1.558 0.364 0.132 0.802 0.085 0.055 0.146 -seafood food 0.500 2.944 0.909 0.805 6.109 0.790 0.235 0.796 -seafood lobster 0.333 2.539 0.857 0.667 9.360 0.809 0.185 0.801 -lobster food 0.250 2.251 0.769 0.540 6.109 0.615 0.115 0.566 -lobster wine 0.091 1.240 0.375 0.113 3.337 0.297 0.059 0.174 -food preparation 0.143 1.692 0.571 0.285 3.337 0.461 0.114 0.231 -video archive 0.111 1.440 0.556 0.199 2.306 0.206 0.053 0.173 -start year 0.167 1.846 0.545 0.307 2.691 0.385 0.104 0.267 -start match 0.143 1.692 0.571 0.285 2.691 0.307 0.078 0.233 -game round 0.333 2.539 0.889 0.670 6.964 0.744 0.173 0.662 -boxing round 0.143 1.692 0.727 0.301 7.433 0.688 0.129 0.467 -championship tournament 0.333 2.539 0.857 0.667 6.609 0.633 0.115 0.686 -line insurance 0.200 2.028 0.714 0.442 5.176 0.559 0.121 0.442 -day summer 0.250 2.251 0.769 0.540 3.800 0.474 0.106 0.438 -summer drought 0.250 2.251 0.769 0.540 3.800 0.351 0.066 0.438 -summer nature 0.111 1.440 0.333 0.108 0.596 0.072 0.061 0.125 -day dawn 0.333 2.539 0.833 0.659 3.800 0.429 0.090 0.539 -nature environment 0.167 1.846 0.545 0.307 2.298 0.336 0.099 0.250 -environment ecology 0.500 2.944 0.923 0.815 6.464 0.000 0.000 0.809 -nature man 0.250 2.251 0.667 0.458 2.283 0.301 0.086 0.357 -man woman 0.333 2.539 0.706 0.670 4.910 0.787 0.273 0.599 -man governor 0.200 2.028 0.667 0.449 3.258 0.402 0.093 0.296 -murder manslaughter 0.333 2.539 0.917 0.670 8.560 0.785 0.176 0.772 -soap opera 0.071 0.999 0.235 0.040 0.802 0.075 0.048 0.081 -opera performance 0.100 1.335 0.308 0.089 0.802 0.084 0.057 0.117 -life lesson 0.125 1.558 0.429 0.168 2.620 0.310 0.079 0.183 -focus life 0.143 1.692 0.533 0.251 3.140 0.371 0.086 0.224 -production crew 0.125 1.558 0.364 0.132 0.596 0.070 0.065 0.140 -television film 0.200 2.028 0.778 0.449 7.021 0.720 0.155 0.545 -lover quarrel 0.077 1.073 0.400 0.086 1.532 0.169 0.062 0.105 -viewer serial 0.091 1.240 0.500 0.133 2.306 0.212 0.061 0.143 -possibility girl 0.091 1.240 0.167 0.000 0.000 0.000 0.068 0.091 -population development 0.143 1.692 0.727 0.301 5.654 0.642 0.137 0.370 -morality importance 0.200 2.028 0.667 0.425 3.965 0.583 0.150 0.377 -morality marriage 0.167 1.846 0.545 0.307 2.298 0.306 0.088 0.250 -Mexico Brazil 0.200 2.028 0.800 0.449 6.134 0.495 0.074 0.496 -gender equality 0.125 1.558 0.462 0.206 2.298 0.234 0.062 0.193 -change attitude 0.167 1.846 0.545 0.307 3.037 0.350 0.126 0.283 -family planning 0.100 1.335 0.308 0.089 0.596 0.082 0.070 0.113 -opera industry 0.091 1.240 0.286 0.073 0.596 0.065 0.055 0.103 -sugar approach 0.091 1.240 0.286 0.073 0.802 0.087 0.056 0.107 -practice institution 0.333 2.539 0.833 0.659 7.355 0.871 0.315 0.721 -ministry culture 0.125 1.558 0.533 0.233 3.336 0.314 0.064 0.231 -problem challenge 0.167 1.846 0.615 0.348 3.848 0.421 0.086 0.287 -size prominence 0.167 1.846 0.615 0.348 3.140 0.312 0.074 0.287 -country citizen 0.111 1.440 0.429 0.168 1.290 0.178 0.077 0.143 -planet people 0.111 1.440 0.200 0.000 0.000 0.000 0.072 0.111 -development issue 0.250 2.251 0.750 0.458 4.089 0.567 0.138 0.454 -experience music 0.167 1.846 0.615 0.348 2.645 0.329 0.086 0.240 -music project 0.250 2.251 0.800 0.546 3.144 0.432 0.108 0.402 -glass metal 0.167 1.846 0.545 0.307 3.337 0.454 0.111 0.296 -aluminum metal 0.500 2.944 0.933 0.818 5.442 0.667 0.155 0.771 -chance credibility 0.167 1.846 0.500 0.251 2.298 0.268 0.074 0.218 -exhibit memorabilia 0.111 1.440 0.429 0.168 2.620 0.214 0.049 0.183 -concert virtuoso 0.077 1.073 0.143 0.000 0.000 0.000 0.048 0.077 -rock jazz 0.333 2.539 0.875 0.669 8.409 0.864 0.275 0.766 -museum theater 0.143 1.692 0.625 0.296 2.306 0.248 0.067 0.218 -observation architecture 0.167 1.846 0.706 0.366 3.144 0.295 0.067 0.287 -space world 0.200 2.028 0.571 0.307 2.298 0.334 0.099 0.222 -preservation world 0.143 1.692 0.588 0.243 4.235 0.445 0.086 0.269 -admission ticket 0.143 1.692 0.571 0.285 3.848 0.360 0.068 0.282 -shower thunderstorm 0.143 1.692 0.700 0.301 6.561 0.000 0.000 0.419 -shower flood 0.125 1.558 0.636 0.243 4.936 0.451 0.077 0.273 -weather forecast 0.071 0.999 0.133 0.000 0.000 0.000 0.052 0.071 -disaster area 0.111 1.440 0.500 0.191 3.140 0.353 0.080 0.201 -governor office 0.100 1.335 0.526 0.163 2.306 0.250 0.067 0.157 -architecture century 0.100 1.335 0.308 0.089 0.596 0.063 0.054 0.113 diff --git a/sematch/eval/word_similarity/results_original/readme.txt b/sematch/eval/word_similarity/results_original/readme.txt deleted file mode 100644 index 7a5a334..0000000 --- a/sematch/eval/word_similarity/results_original/readme.txt +++ /dev/null @@ -1,3 +0,0 @@ -dataset RG MC WS353 WS353-Sim SimLex -ori-pairs 65 30 353 203 999 -noun-pairs 65 30 348 201 666 diff --git a/sematch/eval/word_similarity/results_original/type_mc.txt b/sematch/eval/word_similarity/results_original/type_mc.txt deleted file mode 100644 index 6f64957..0000000 --- a/sematch/eval/word_similarity/results_original/type_mc.txt +++ /dev/null @@ -1,19 +0,0 @@ -word1 word2 path lch wup li res res_graph lin lin_graph jcn jcn_graph wpath wpath_graph -car automobile 1.000 3.638 1.000 1.000 7.591 7.386 1.000 1.000 1.000 14.869 1.000 1.000 -gem jewel 1.000 3.638 1.000 1.000 12.068 12.876 1.000 1.000 1.000 25.681 1.000 1.000 -journey voyage 0.500 2.944 0.952 0.819 6.826 9.326 0.778 0.833 0.204 5.911 0.821 0.889 -coast shore 0.500 2.944 0.909 0.805 9.416 11.560 0.963 0.992 0.582 1.187 0.891 0.930 -magician wizard 1.000 3.638 0.667 1.000 11.981 14.175 1.000 1.000 1.000 23.732 1.000 1.000 -food fruit 0.100 1.335 0.400 0.138 1.593 3.724 0.161 0.108 0.084 17.002 0.137 0.203 -bird cock 0.500 2.944 0.952 0.819 7.678 9.339 0.774 0.108 0.182 26.511 0.847 0.889 -tool implement 0.500 2.944 0.933 0.818 5.877 10.564 0.947 0.991 0.604 1.199 0.788 0.914 -brother monk 0.500 2.944 0.952 0.819 9.262 8.392 0.986 0.165 0.797 18.254 0.888 0.867 -journey car 0.056 0.747 0.105 0.000 0.000 0.000 0.000 0.000 0.065 22.709 0.056 0.056 -monk oracle 0.125 1.558 0.571 0.246 2.334 1.540 0.226 0.164 0.059 16.711 0.194 0.168 -cemetery woodland 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.087 0.052 21.050 0.143 0.134 -coast hill 0.200 2.028 0.667 0.425 5.885 5.283 0.599 0.521 0.113 26.112 0.482 0.448 -forest graveyard 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.087 0.052 21.050 0.143 0.134 -shore woodland 0.200 2.028 0.600 0.375 1.290 0.955 0.136 0.077 0.057 23.845 0.250 0.236 -monk slave 0.200 2.028 0.667 0.449 2.334 1.540 0.254 0.174 0.068 15.571 0.296 0.261 -coast forest 0.167 1.846 0.545 0.307 1.290 2.612 0.131 0.077 0.055 24.032 0.211 0.198 -glass magician 0.125 1.558 0.471 0.206 2.283 1.532 0.214 0.125 0.056 23.258 0.192 0.167 diff --git a/sematch/eval/word_similarity/results_original/type_rg.txt b/sematch/eval/word_similarity/results_original/type_rg.txt deleted file mode 100644 index e334a63..0000000 --- a/sematch/eval/word_similarity/results_original/type_rg.txt +++ /dev/null @@ -1,42 +0,0 @@ -word1 word2 path lch wup li res res_graph lin lin_graph jcn jcn_graph wpath wpath_graph -gem jewel 1.000 3.638 1.000 1.000 12.068 12.876 1.000 1.000 1.000 25.681 1.000 1.000 -automobile car 1.000 3.638 1.000 1.000 7.591 7.386 1.000 1.000 1.000 14.869 1.000 1.000 -cemetery graveyard 1.000 3.638 1.000 1.000 11.288 8.765 1.000 1.000 1.000 1.000 1.000 1.000 -cushion pillow 0.500 2.944 0.933 0.818 11.288 15.274 0.977 1.000 0.650 1.000 0.925 0.968 -implement tool 0.500 2.944 0.933 0.818 5.877 10.564 0.947 0.991 0.604 1.199 0.788 0.914 -forest woodland 1.000 3.638 0.833 0.995 9.614 13.194 1.000 1.000 1.000 18.262 1.000 1.000 -coast shore 0.500 2.944 0.909 0.805 9.416 11.560 0.963 0.992 0.582 1.187 0.891 0.930 -journey voyage 0.500 2.944 0.952 0.819 6.826 9.326 0.778 0.833 0.204 5.911 0.821 0.889 -serf slave 0.250 2.251 0.632 0.549 9.090 9.258 0.795 0.851 0.176 4.244 0.717 0.725 -hill mound 1.000 3.638 1.000 1.000 9.966 15.274 1.000 1.000 1.000 30.449 1.000 1.000 -magician wizard 1.000 3.638 0.667 1.000 11.981 14.175 1.000 1.000 1.000 23.732 1.000 1.000 -brother monk 0.500 2.944 0.952 0.819 9.262 8.392 0.986 0.165 0.797 18.254 0.888 0.867 -food fruit 0.100 1.335 0.400 0.138 1.593 3.724 0.161 0.108 0.084 17.002 0.137 0.203 -bird cock 0.500 2.944 0.952 0.819 7.678 9.339 0.774 0.108 0.182 26.511 0.847 0.889 -oracle sage 0.167 1.846 0.571 0.368 6.513 3.390 0.525 0.302 0.078 16.673 0.461 0.299 -sage wizard 0.167 1.846 0.632 0.367 2.334 1.540 0.205 0.130 0.053 24.151 0.252 0.220 -magician oracle 0.143 1.692 0.600 0.301 2.334 1.540 0.199 0.158 0.051 22.494 0.219 0.190 -glass jewel 0.143 1.692 0.588 0.251 2.306 2.729 0.237 0.233 0.063 18.955 0.217 0.208 -cemetery mound 0.091 1.240 0.375 0.113 1.290 0.955 0.129 0.079 0.054 23.940 0.118 0.110 -car journey 0.056 0.747 0.105 0.000 0.000 0.000 0.000 0.000 0.065 22.709 0.056 0.056 -hill woodland 0.167 1.846 0.545 0.307 1.290 0.955 0.141 0.088 0.060 27.559 0.211 0.198 -furnace implement 0.125 1.558 0.588 0.243 2.306 2.729 0.271 0.238 0.075 18.489 0.193 0.208 -coast hill 0.200 2.028 0.667 0.425 5.885 5.283 0.599 0.521 0.113 26.112 0.482 0.448 -bird woodland 0.111 1.440 0.400 0.138 1.290 2.612 0.149 0.085 0.064 26.223 0.130 0.166 -shore voyage 0.067 0.930 0.125 0.000 0.000 0.000 0.000 0.000 0.047 26.448 0.067 0.067 -cemetery woodland 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.087 0.052 21.050 0.143 0.134 -forest graveyard 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.087 0.052 21.050 0.143 0.134 -mound shore 0.200 2.028 0.667 0.425 5.885 5.283 0.565 0.071 0.099 26.735 0.482 0.448 -automobile cushion 0.111 1.440 0.600 0.201 3.258 3.625 0.356 0.241 0.078 18.201 0.205 0.219 -boy sage 0.167 1.846 0.632 0.367 2.334 1.540 0.220 0.150 0.057 20.772 0.252 0.220 -monk oracle 0.125 1.558 0.571 0.246 2.334 1.540 0.226 0.164 0.059 16.711 0.194 0.168 -shore woodland 0.200 2.028 0.600 0.375 1.290 0.955 0.136 0.077 0.057 23.845 0.250 0.236 -coast forest 0.167 1.846 0.545 0.307 1.290 2.612 0.131 0.077 0.055 24.032 0.211 0.198 -asylum cemetery 0.083 1.153 0.353 0.092 1.290 0.955 0.124 0.099 0.052 23.129 0.108 0.101 -monk slave 0.200 2.028 0.667 0.449 2.334 1.540 0.254 0.174 0.068 15.571 0.296 0.261 -cushion jewel 0.143 1.692 0.625 0.296 2.306 2.729 0.223 0.194 0.059 23.691 0.218 0.235 -glass magician 0.125 1.558 0.471 0.206 2.283 1.532 0.214 0.125 0.056 23.258 0.192 0.167 -asylum monk 0.091 1.240 0.444 0.128 1.532 1.227 0.164 0.130 0.060 22.212 0.123 0.116 -asylum fruit 0.143 1.692 0.625 0.296 2.306 2.729 0.237 0.121 0.068 23.596 0.218 0.235 -automobile wizard 0.077 1.073 0.400 0.086 1.532 1.227 0.181 0.129 0.067 20.107 0.105 0.099 -fruit furnace 0.111 1.440 0.556 0.199 2.306 2.729 0.218 0.111 0.061 20.706 0.173 0.187 diff --git a/sematch/eval/word_similarity/results_original/type_simlex.txt b/sematch/eval/word_similarity/results_original/type_simlex.txt deleted file mode 100644 index 22cb524..0000000 --- a/sematch/eval/word_similarity/results_original/type_simlex.txt +++ /dev/null @@ -1,409 +0,0 @@ -word1 word2 path lch wup li res res_graph lin lin_graph jcn jcn_graph wpath wpath_graph -book text 0.500 2.944 0.952 0.819 7.349 6.954 0.892 0.637 0.359 23.257 0.838 0.786 -groom bride 0.333 2.539 0.909 0.670 10.423 15.274 0.917 0.101 0.346 28.468 0.837 0.938 -night day 0.333 2.539 0.833 0.659 5.886 9.521 0.769 0.654 0.220 13.283 0.650 0.807 -plane airport 0.111 1.440 0.556 0.199 2.306 2.729 0.252 0.315 0.068 12.855 0.173 0.187 -horse mare 0.500 2.944 0.968 0.819 7.739 10.144 0.804 0.999 0.209 1.030 0.849 0.906 -bottom top 0.333 2.539 0.900 0.670 8.255 13.194 0.871 0.456 0.290 29.756 0.759 0.905 -friend buddy 0.500 2.944 0.706 0.818 7.142 6.943 0.866 0.735 0.312 18.492 0.831 0.825 -student pupil 1.000 3.638 0.667 1.000 7.686 9.725 1.000 1.000 1.000 11.057 1.000 1.000 -world globe 1.000 3.638 1.000 1.000 7.690 8.119 1.000 0.188 1.000 28.839 1.000 1.000 -plane jet 0.500 2.944 0.960 0.819 8.871 11.025 0.894 0.972 0.323 1.638 0.879 0.921 -woman man 0.333 2.539 0.706 0.670 4.910 4.037 0.787 0.505 0.273 18.995 0.599 0.552 -actress actor 0.500 2.944 0.952 0.819 6.547 4.287 0.939 0.500 0.541 19.057 0.812 0.722 -teacher instructor 1.000 3.638 1.000 1.000 8.890 6.626 1.000 1.000 1.000 1.000 1.000 1.000 -movie film 1.000 3.638 1.000 1.000 7.817 4.472 1.000 1.000 1.000 12.985 1.000 1.000 -bird hawk 0.333 2.539 0.909 0.670 7.678 9.339 0.816 0.833 0.224 26.105 0.735 0.801 -word dictionary 0.056 0.747 0.105 0.000 0.000 0.000 0.000 0.000 0.056 24.954 0.056 0.056 -dog cat 0.200 2.028 0.857 0.449 7.912 9.490 0.877 0.841 0.310 5.241 0.594 0.675 -area region 1.000 3.638 1.000 0.995 7.270 13.664 1.000 1.000 1.000 24.060 1.000 1.000 -navy army 0.333 2.539 0.889 0.670 7.896 9.382 0.798 0.860 0.200 4.052 0.744 0.802 -child adult 0.333 2.539 0.750 0.669 2.334 1.540 0.397 0.254 0.124 10.163 0.457 0.413 -book story 0.200 2.028 0.714 0.442 5.646 7.501 0.704 0.617 0.174 23.051 0.468 0.571 -taxi cab 1.000 3.638 1.000 1.000 10.882 13.328 1.000 1.000 1.000 1.000 1.000 1.000 -bed bedroom 0.167 1.846 0.667 0.362 3.979 4.386 0.413 0.205 0.081 23.316 0.258 0.275 -roof ceiling 1.000 3.638 1.000 1.000 9.645 2.729 1.000 0.187 1.000 24.703 1.000 1.000 -disease infection 0.250 2.251 0.842 0.549 6.715 7.004 0.743 0.762 0.177 5.375 0.599 0.614 -boat anchor 0.111 1.440 0.600 0.201 3.258 3.625 0.321 0.349 0.068 14.515 0.205 0.219 -priest monk 0.143 1.692 0.600 0.301 2.334 1.540 0.267 0.221 0.072 16.361 0.219 0.190 -river stream 0.500 2.944 0.909 0.805 6.762 5.575 0.986 0.999 0.841 20.247 0.819 0.776 -date calendar 0.111 1.440 0.400 0.138 2.855 3.353 0.279 0.197 0.064 22.297 0.174 0.190 -sea ocean 1.000 3.638 1.000 0.995 9.598 5.070 1.000 0.439 1.000 13.980 1.000 1.000 -wood log 0.500 2.944 0.941 0.818 8.502 12.709 0.855 0.206 0.258 28.215 0.870 0.945 -mud dirt 0.500 2.944 0.941 0.818 8.311 10.930 0.930 0.925 0.446 19.315 0.865 0.920 -hallway corridor 0.500 2.944 0.947 0.819 9.167 11.777 0.983 0.997 0.761 1.063 0.886 0.933 -way manner 1.000 3.638 1.000 0.984 6.652 9.413 1.000 1.000 1.000 24.994 1.000 1.000 -mouse cat 0.200 2.028 0.815 0.449 5.414 7.651 0.527 0.613 0.093 11.304 0.401 0.524 -death burial 0.125 1.558 0.533 0.233 2.332 4.982 0.223 0.356 0.061 19.338 0.184 0.255 -music melody 0.500 2.944 0.909 0.805 6.460 12.441 0.876 0.681 0.406 6.187 0.760 0.775 -beer alcohol 0.333 2.539 0.900 0.670 7.585 8.865 0.887 0.804 0.341 8.348 0.731 0.783 -storm hurricane 0.250 2.251 0.842 0.549 10.322 10.943 0.898 0.999 0.298 1.027 0.769 0.793 -tax income 0.100 1.335 0.471 0.157 4.470 8.737 0.602 0.665 0.145 9.797 0.232 0.438 -floor ceiling 0.333 2.539 0.923 0.670 8.031 10.564 0.867 0.692 0.288 10.419 0.750 0.679 -beach seashore 0.250 2.251 0.727 0.520 5.885 5.283 0.577 0.520 0.104 10.761 0.553 0.520 -hound fox 0.200 2.028 0.867 0.449 8.886 10.383 0.761 0.769 0.152 26.390 0.645 0.717 -car horn 0.167 1.846 0.706 0.366 3.258 3.625 0.359 0.347 0.079 20.965 0.293 0.310 -friend guest 0.200 2.028 0.667 0.449 2.334 1.540 0.275 0.178 0.075 16.868 0.296 0.261 -employer employee 0.200 2.028 0.667 0.449 2.334 1.540 0.292 0.182 0.081 14.885 0.296 0.261 -ball cannon 0.250 2.251 0.783 0.540 6.763 8.747 0.608 0.260 0.103 27.299 0.475 0.642 -alcohol brandy 0.333 2.539 0.900 0.670 7.585 8.865 0.782 0.779 0.191 9.041 0.731 0.783 -victory triumph 1.000 3.638 1.000 0.999 8.479 9.300 1.000 1.000 1.000 1.000 1.000 1.000 -motel inn 0.250 2.251 0.842 0.549 8.372 7.693 0.767 0.578 0.165 12.249 0.683 0.650 -clothes cloth 0.200 2.028 0.714 0.442 2.306 2.729 0.290 0.211 0.081 21.445 0.295 0.315 -band orchestra 0.333 2.539 0.857 0.667 8.064 5.446 0.805 0.645 0.204 25.892 0.751 0.628 -business industry 0.500 2.944 0.947 0.819 6.538 10.422 0.918 0.997 0.461 16.846 0.811 0.911 -alcohol gin 0.333 2.539 0.900 0.670 7.585 8.865 0.821 0.756 0.233 9.734 0.731 0.783 -hat coat 0.200 2.028 0.762 0.447 6.125 9.840 0.645 0.769 0.129 8.450 0.440 0.686 -window door 0.200 2.028 0.750 0.447 3.979 4.386 0.448 0.322 0.092 19.480 0.378 0.399 -glass crystal 0.333 2.539 0.900 0.670 9.422 8.066 0.914 0.078 0.365 23.007 0.804 0.752 -wine brandy 0.250 2.251 0.857 0.549 7.585 8.865 0.738 0.660 0.157 10.128 0.644 0.707 -creator maker 1.000 3.638 1.000 1.000 7.276 3.326 1.000 0.698 1.000 11.459 1.000 1.000 -arm muscle 0.125 1.558 0.588 0.243 4.402 7.536 0.472 0.100 0.092 21.887 0.276 0.434 -death tragedy 0.167 1.846 0.667 0.362 4.053 7.955 0.474 0.687 0.100 20.448 0.331 0.541 -gun cannon 0.500 2.944 0.952 0.819 9.251 12.635 0.893 1.000 0.311 25.355 0.887 0.944 -grass blade 0.143 1.692 0.600 0.301 3.337 5.761 0.325 0.119 0.067 21.598 0.219 0.247 -hose garden 0.077 1.073 0.333 0.076 1.290 0.955 0.125 0.078 0.052 23.654 0.100 0.093 -boy kid 0.333 2.539 0.909 0.670 6.838 8.082 0.934 0.977 0.508 16.922 0.697 0.752 -church choir 0.167 1.846 0.706 0.366 3.979 4.386 0.385 0.000 0.073 17.344 0.327 0.347 -clothes drawer 0.143 1.692 0.625 0.296 2.306 2.729 0.220 0.114 0.062 20.129 0.218 0.235 -tower bell 0.200 2.028 0.714 0.442 4.836 7.402 0.464 0.299 0.082 18.025 0.424 0.566 -father parent 0.500 2.944 0.960 0.819 7.519 9.162 0.947 0.973 0.542 1.517 0.843 0.885 -school grade 0.200 2.028 0.667 0.425 5.176 10.445 0.605 0.318 0.129 26.067 0.345 0.595 -parent adult 0.250 2.251 0.800 0.546 2.334 1.540 0.376 0.233 0.114 11.120 0.354 0.317 -bar jail 0.125 1.558 0.632 0.245 3.979 4.386 0.372 0.331 0.069 26.829 0.258 0.275 -car highway 0.125 1.558 0.588 0.243 2.306 2.729 0.272 0.347 0.075 16.278 0.193 0.208 -dictionary definition 0.053 0.693 0.100 0.000 0.000 0.000 0.000 0.000 0.046 24.424 0.053 0.053 -army legion 0.500 2.944 0.941 0.818 9.167 9.721 0.932 0.886 0.426 3.508 0.886 0.897 -chair bench 0.333 2.539 0.900 0.670 8.435 13.077 0.835 0.181 0.231 29.350 0.767 0.902 -boy son 1.000 3.638 0.833 1.000 7.806 8.455 1.000 1.000 1.000 3.840 1.000 1.000 -water ice 0.200 2.028 0.625 0.307 5.279 7.383 0.552 0.112 0.106 25.904 0.351 0.464 -attorney lawyer 1.000 3.638 1.000 1.000 8.414 5.552 1.000 1.000 1.000 1.000 1.000 1.000 -area zone 0.250 2.251 0.769 0.540 4.402 7.536 0.457 0.313 0.098 28.079 0.471 0.642 -business company 0.250 2.251 0.769 0.540 3.907 3.505 0.551 0.646 0.136 17.127 0.444 0.422 -clothes fabric 0.200 2.028 0.714 0.442 2.306 2.729 0.290 0.211 0.081 21.445 0.295 0.315 -sweater jacket 0.200 2.028 0.818 0.449 7.107 10.277 0.663 0.849 0.121 4.666 0.550 0.712 -money capital 0.167 1.846 0.615 0.348 4.470 8.737 0.528 0.202 0.111 22.180 0.352 0.584 -hand foot 0.250 2.251 0.857 0.549 7.600 14.581 0.964 0.932 0.640 26.223 0.645 0.896 -alcohol cocktail 0.333 2.539 0.900 0.670 7.585 8.865 0.850 0.793 0.272 8.635 0.731 0.783 -molecule atom 1.000 3.638 1.000 0.999 9.095 12.278 1.000 1.000 1.000 19.765 1.000 1.000 -lens camera 0.250 2.251 0.842 0.549 7.549 10.277 0.673 0.284 0.120 22.507 0.642 0.768 -meal dinner 0.500 2.944 0.933 0.818 8.233 12.278 0.908 0.000 0.375 27.165 0.863 0.939 -eye tear 0.143 1.692 0.571 0.285 3.012 3.724 0.375 0.000 0.091 27.117 0.246 0.222 -god devil 0.333 2.539 0.875 0.669 5.739 8.997 0.676 0.868 0.154 26.290 0.643 0.788 -loop belt 0.500 2.944 0.947 0.819 9.049 12.138 0.973 1.000 0.668 27.719 0.883 0.938 -rat mouse 0.333 2.539 0.923 0.670 10.659 9.104 0.907 0.770 0.314 25.948 0.844 0.792 -motor engine 0.500 2.944 0.947 0.819 8.849 9.475 0.976 0.994 0.694 12.605 0.878 0.892 -car cab 0.500 2.944 0.960 0.819 7.591 11.942 0.822 0.713 0.233 20.811 0.845 0.878 -cat lion 0.500 2.944 0.966 0.819 9.841 12.278 0.943 0.983 0.456 3.611 0.900 0.939 -door gate 0.333 2.539 0.900 0.670 8.343 11.585 0.897 0.897 0.344 3.668 0.763 0.869 -cat pet 0.250 2.251 0.857 0.549 10.388 4.834 0.922 0.393 0.363 16.582 0.772 0.269 -bone jaw 0.500 2.944 0.947 0.819 8.435 9.451 0.873 0.847 0.290 4.425 0.868 0.892 -house key 0.143 1.692 0.625 0.296 3.012 3.724 0.355 0.290 0.084 25.479 0.246 0.235 -door corridor 0.143 1.692 0.667 0.300 5.654 6.765 0.637 0.210 0.134 21.494 0.370 0.430 -god spirit 0.333 2.539 0.875 0.669 5.739 8.997 0.808 0.916 0.268 26.191 0.643 0.788 -wall brick 0.200 2.028 0.714 0.442 2.306 2.729 0.234 0.193 0.062 23.786 0.295 0.315 -horn drum 0.200 2.028 0.778 0.449 7.544 10.211 0.697 0.521 0.132 13.415 0.518 0.661 -phrase word 0.250 2.251 0.769 0.540 6.182 9.547 0.710 0.421 0.165 18.601 0.570 0.737 -band parade 0.167 1.846 0.615 0.348 5.053 7.853 0.463 0.355 0.079 27.910 0.382 0.536 -cloud storm 0.333 2.539 0.875 0.669 6.561 10.819 0.637 0.844 0.118 4.875 0.684 0.848 -beer beverage 0.250 2.251 0.842 0.549 6.693 8.510 0.826 0.784 0.262 5.684 0.597 0.690 -intelligence logic 0.167 1.846 0.667 0.362 4.861 6.634 0.495 0.272 0.092 21.119 0.372 0.468 -communication language 0.500 2.944 0.857 0.683 3.037 5.511 0.623 0.814 0.240 21.845 0.642 0.729 -mob crowd 0.500 2.944 0.923 0.815 8.245 15.274 0.830 0.255 0.228 21.112 0.863 0.968 -shore coast 0.500 2.944 0.909 0.805 9.416 11.560 0.963 0.992 0.582 1.187 0.891 0.930 -water salt 0.250 2.251 0.842 0.549 6.101 7.383 0.690 0.565 0.154 12.659 0.565 0.587 -sense intuition 0.143 1.692 0.625 0.296 4.385 9.003 0.499 0.388 0.102 17.939 0.307 0.554 -situation condition 0.500 2.944 0.909 0.805 4.235 6.780 0.728 0.807 0.240 20.793 0.720 0.820 -crime theft 0.333 2.539 0.900 0.670 7.427 10.174 0.934 0.866 0.488 4.154 0.724 0.829 -style fashion 1.000 3.638 1.000 0.984 8.321 13.077 1.000 1.000 1.000 18.265 1.000 1.000 -boundary border 0.500 2.944 0.933 0.818 8.528 9.055 0.940 0.948 0.479 1.998 0.870 0.883 -arm body 0.250 2.251 0.800 0.546 4.402 7.536 0.498 0.365 0.111 24.298 0.448 0.518 -boat car 0.200 2.028 0.778 0.449 5.922 6.699 0.728 0.755 0.185 16.269 0.444 0.527 -bride princess 0.125 1.558 0.571 0.246 2.334 1.540 0.211 0.132 0.054 21.208 0.194 0.168 -car gauge 0.125 1.558 0.632 0.245 3.258 3.625 0.351 0.320 0.077 22.757 0.228 0.243 -insect bee 0.333 2.539 0.909 0.670 7.809 6.746 0.862 0.789 0.286 4.615 0.741 0.693 -animal person 0.333 2.539 0.857 0.667 2.224 1.480 0.611 0.464 0.261 4.413 0.451 0.410 -bed hospital 0.167 1.846 0.667 0.362 3.979 4.386 0.432 0.247 0.087 18.739 0.258 0.275 -disc computer 0.200 2.028 0.778 0.449 4.076 6.754 0.464 0.610 0.096 26.210 0.383 0.530 -grass moss 0.125 1.558 0.632 0.245 2.224 1.480 0.212 0.150 0.057 20.678 0.190 0.166 -school law 0.167 1.846 0.667 0.362 3.907 5.359 0.498 0.412 0.113 27.048 0.324 0.304 -foot head 0.333 2.539 0.857 0.667 6.061 13.887 0.722 0.097 0.185 30.854 0.659 0.917 -mother guardian 0.143 1.692 0.600 0.301 2.334 1.540 0.303 0.190 0.085 14.126 0.219 0.190 -alcohol whiskey 0.333 2.539 0.900 0.670 7.585 8.865 0.880 0.779 0.326 9.041 0.731 0.783 -bathroom bedroom 0.333 2.539 0.889 0.670 5.408 8.216 0.665 0.592 0.155 12.343 0.626 0.758 -plate bowl 0.333 2.539 0.833 0.659 7.490 9.016 0.678 0.284 0.123 21.772 0.678 0.598 -worker employer 0.250 2.251 0.706 0.548 2.334 1.540 0.345 0.233 0.101 15.796 0.359 0.320 -son father 0.333 2.539 0.909 0.670 7.439 7.095 0.893 0.783 0.360 4.943 0.724 0.448 -heart surgery 0.111 1.440 0.556 0.199 3.292 5.771 0.320 0.280 0.069 20.791 0.207 0.312 -woman secretary 0.167 1.846 0.632 0.367 4.518 3.622 0.499 0.237 0.099 16.230 0.281 0.243 -man father 0.250 2.251 0.706 0.548 2.334 2.612 0.329 0.217 0.095 22.805 0.359 0.320 -beach island 0.200 2.028 0.600 0.375 1.290 0.955 0.152 0.125 0.065 14.414 0.250 0.236 -story topic 0.333 2.539 0.800 0.635 3.848 6.513 0.524 0.669 0.125 12.367 0.541 0.681 -game fun 0.333 2.539 0.875 0.669 5.585 5.738 0.566 0.252 0.104 30.854 0.635 0.643 -couple pair 1.000 3.638 1.000 1.000 9.614 8.604 1.000 1.000 1.000 1.000 1.000 1.000 -woman wife 0.500 2.944 0.632 0.819 6.257 5.869 0.850 0.758 0.312 4.749 0.802 0.787 -ceiling cathedral 0.100 1.335 0.526 0.163 2.306 2.729 0.205 0.229 0.053 19.403 0.157 0.170 -wood paper 0.250 2.251 0.800 0.546 4.693 6.270 0.654 0.501 0.167 24.105 0.487 0.575 -top side 0.500 2.944 0.947 0.819 6.825 13.194 0.878 0.473 0.344 30.854 0.821 0.950 -crime fraud 0.500 2.944 0.947 0.819 7.427 10.174 0.823 0.917 0.239 21.853 0.840 0.906 -pain harm 0.125 1.558 0.533 0.233 3.140 6.399 0.382 0.216 0.090 19.988 0.224 0.373 -lover companion 0.250 2.251 0.706 0.548 2.334 1.540 0.302 0.189 0.085 17.517 0.359 0.320 -father daughter 0.167 1.846 0.632 0.367 5.710 7.095 0.683 0.749 0.159 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20.100 0.310 0.402 -danger disease 0.143 1.692 0.625 0.296 4.235 6.780 0.488 0.000 0.101 20.371 0.300 0.431 -accident catastrophe 0.250 2.251 0.824 0.548 7.958 10.391 0.836 0.945 0.243 8.951 0.663 0.772 -activity movement 0.200 2.028 0.750 0.447 5.841 13.328 0.768 0.681 0.221 27.992 0.479 0.830 -gossip news 0.111 1.440 0.471 0.168 2.620 4.436 0.268 0.350 0.065 18.925 0.183 0.252 -father god 0.250 2.251 0.842 0.549 7.035 8.997 0.879 0.101 0.340 22.805 0.616 0.598 -action course 0.500 2.944 0.923 0.815 3.734 6.132 0.669 0.613 0.213 26.400 0.697 0.797 -fever illness 0.100 1.335 0.471 0.157 3.140 6.399 0.315 0.278 0.068 14.559 0.183 0.317 -aviation flight 0.500 2.944 0.952 0.819 8.435 9.151 0.968 0.200 0.643 21.892 0.868 0.658 -game action 0.250 2.251 0.800 0.546 3.258 5.519 0.436 0.806 0.142 25.927 0.402 0.533 -molecule air 0.200 2.028 0.571 0.442 3.886 6.149 0.422 0.227 0.086 20.066 0.373 0.496 -home state 0.250 2.251 0.800 0.546 4.752 6.399 0.685 0.613 0.186 26.644 0.490 0.582 -adult guardian 0.250 2.251 0.706 0.548 2.334 1.540 0.390 0.328 0.120 7.308 0.359 0.320 -newspaper information 0.100 1.335 0.400 0.138 2.855 3.353 0.332 0.000 0.080 17.630 0.174 0.190 -communication television 0.083 1.153 0.154 0.000 0.000 0.000 0.000 0.000 0.074 18.098 0.083 0.083 -author reader 0.200 2.028 0.667 0.449 2.334 1.540 0.304 0.234 0.085 15.213 0.296 0.261 -ballad song 0.500 2.944 0.933 0.818 8.298 5.896 0.934 0.831 0.459 3.406 0.864 0.788 -wall decoration 0.250 2.251 0.769 0.540 2.306 2.729 0.302 0.235 0.086 26.606 0.358 0.380 -nurse scientist 0.200 2.028 0.667 0.449 2.334 1.540 0.276 0.234 0.076 14.605 0.296 0.261 -politician president 0.250 2.251 0.632 0.549 4.969 3.258 0.663 0.444 0.165 12.558 0.503 0.408 -president mayor 0.200 2.028 0.600 0.449 4.969 3.258 0.529 0.394 0.102 11.024 0.431 0.341 -book essay 0.250 2.251 0.769 0.540 4.263 5.519 0.557 0.501 0.129 22.889 0.463 0.533 -man warrior 0.333 2.539 0.750 0.669 2.334 1.540 0.294 0.221 0.082 22.497 0.457 0.413 -article journal 0.250 2.251 0.769 0.540 4.583 5.519 0.512 0.000 0.103 20.460 0.463 0.533 -crowd parade 0.143 1.692 0.500 0.251 2.855 3.353 0.290 0.243 0.067 21.896 0.240 0.260 -teacher rabbi 0.125 1.558 0.571 0.246 2.334 2.612 0.226 0.145 0.059 19.127 0.194 0.168 -book article 0.333 2.539 0.857 0.667 5.958 6.954 0.715 0.377 0.174 19.397 0.654 0.702 -room cell 0.500 2.944 0.941 0.818 5.408 8.216 0.804 0.751 0.276 19.322 0.770 0.862 -daughter kid 0.333 2.539 0.909 0.670 6.838 8.082 0.900 0.932 0.398 15.383 0.697 0.752 -car elevator 0.143 1.692 0.667 0.300 3.258 3.625 0.353 0.330 0.077 22.064 0.256 0.272 -clothes coat 0.250 2.251 0.800 0.546 6.125 9.840 0.654 0.735 0.134 9.629 0.511 0.745 -apple sauce 0.077 1.073 0.333 0.076 3.337 5.761 0.315 0.506 0.064 12.252 0.149 0.232 -car hose 0.111 1.440 0.556 0.199 2.306 2.729 0.245 0.241 0.066 23.199 0.173 0.187 -tobacco cigarette 0.333 2.539 0.800 0.670 9.104 13.664 0.977 0.982 0.701 1.511 0.792 0.913 -student professor 0.125 1.558 0.571 0.246 2.334 1.540 0.276 0.245 0.076 16.814 0.194 0.168 -baby daughter 0.250 2.251 0.870 0.549 6.838 8.082 0.754 0.690 0.183 8.268 0.605 0.669 -pipe cigar 0.067 0.930 0.222 0.033 0.802 2.612 0.081 0.070 0.052 28.302 0.075 0.113 -box cigar 0.071 0.999 0.235 0.040 0.802 2.612 0.078 0.065 0.050 27.897 0.084 0.121 -cup cone 0.250 2.251 0.824 0.548 6.394 9.625 0.583 0.212 0.098 21.261 0.581 0.741 -bone teeth 0.500 2.944 0.947 0.819 8.435 9.451 0.945 0.893 0.505 3.268 0.868 0.892 -cup jar 0.333 2.539 0.833 0.659 6.440 7.001 0.585 0.565 0.099 11.319 0.678 0.598 -proof fact 0.250 2.251 0.769 0.540 5.081 9.032 0.654 0.591 0.157 26.323 0.509 0.714 -appointment engagement 1.000 3.638 1.000 1.000 9.251 9.560 1.000 0.476 1.000 19.387 1.000 1.000 -birthday year 0.167 1.846 0.667 0.362 3.800 7.890 0.408 0.606 0.083 11.268 0.318 0.538 -word clue 0.200 2.028 0.714 0.442 5.819 9.297 0.684 0.367 0.157 18.601 0.478 0.666 -author creator 0.333 2.539 0.706 0.670 5.691 3.326 0.783 0.431 0.241 5.057 0.640 0.512 -atom carbon 0.250 2.251 0.800 0.546 4.693 6.270 0.453 0.484 0.096 14.308 0.487 0.575 -archbishop bishop 0.500 2.944 0.960 0.819 9.162 5.816 0.926 0.892 0.404 2.413 0.885 0.785 -muscle bone 0.250 2.251 0.824 0.548 7.428 8.789 0.799 0.784 0.212 5.152 0.636 0.703 -muscle tongue 0.250 2.251 0.800 0.546 6.428 9.484 0.629 0.802 0.117 22.911 0.583 0.735 -boy soldier 0.125 1.558 0.571 0.246 2.334 1.540 0.297 0.206 0.083 15.189 0.194 0.168 -bed chair 0.200 2.028 0.800 0.449 6.616 10.855 0.731 0.766 0.170 29.938 0.523 0.738 -clothes jacket 0.200 2.028 0.762 0.447 6.125 9.840 0.622 0.733 0.118 8.179 0.440 0.686 -gun knife 0.333 2.539 0.900 0.670 6.763 10.564 0.770 0.778 0.198 25.222 0.693 0.779 -bottle container 0.333 2.539 0.875 0.669 5.206 6.699 0.702 0.630 0.184 8.881 0.615 0.690 -meat bread 0.250 2.251 0.769 0.540 6.109 8.084 0.772 0.803 0.217 22.359 0.566 0.669 -arm bone 0.111 1.440 0.556 0.199 4.402 7.536 0.504 0.102 0.103 21.566 0.250 0.402 -assignment task 0.500 2.944 0.941 0.818 6.380 12.635 0.855 0.705 0.321 14.460 0.801 0.893 -dinner soup 0.200 2.028 0.750 0.447 6.205 8.301 0.653 0.000 0.132 24.946 0.500 0.614 -dog horse 0.125 1.558 0.759 0.247 5.414 7.651 0.647 0.726 0.145 6.785 0.323 0.441 -arm neck 0.200 2.028 0.750 0.447 5.720 12.971 0.600 0.241 0.116 26.002 0.473 0.819 -ball glove 0.167 1.846 0.737 0.367 5.268 9.016 0.519 0.612 0.093 28.839 0.393 0.599 -nerve brain 0.111 1.440 0.556 0.199 4.402 7.536 0.416 0.796 0.075 25.830 0.250 0.402 -beast animal 1.000 3.638 1.000 0.999 4.946 4.834 1.000 1.000 1.000 1.000 1.000 1.000 -dinner chicken 0.091 1.240 0.353 0.092 3.337 5.761 0.332 0.000 0.069 29.468 0.161 0.247 -girl maid 0.500 2.944 0.952 0.819 7.544 9.297 0.828 0.146 0.242 19.026 0.843 0.888 -child boy 0.333 2.539 0.909 0.670 6.838 8.082 0.934 0.977 0.508 16.922 0.697 0.752 -alcohol wine 0.500 2.944 0.947 0.819 7.585 8.865 0.930 0.812 0.466 8.124 0.845 0.878 -street car 0.111 1.440 0.556 0.199 2.306 2.729 0.284 0.352 0.079 16.064 0.173 0.187 -bell door 0.200 2.028 0.714 0.442 2.306 3.724 0.234 0.201 0.062 22.688 0.295 0.315 -box hat 0.125 1.558 0.588 0.243 2.332 4.982 0.254 0.203 0.069 22.425 0.193 0.216 -belief impression 1.000 3.638 1.000 0.999 7.374 12.501 1.000 1.000 1.000 23.840 1.000 1.000 -bias opinion 0.125 1.558 0.533 0.233 2.645 5.359 0.299 0.422 0.075 21.009 0.205 0.321 -anger mood 0.250 2.251 0.769 0.540 5.073 11.808 0.559 0.810 0.111 6.545 0.508 0.823 -book theme 0.200 2.028 0.714 0.442 5.162 6.513 0.686 0.450 0.175 20.586 0.442 0.517 -friend mother 0.167 1.846 0.632 0.367 6.263 6.672 0.713 0.173 0.166 17.405 0.403 0.425 -car factory 0.125 1.558 0.632 0.245 3.979 4.386 0.440 0.392 0.090 18.378 0.258 0.275 -pact condition 0.200 2.028 0.714 0.442 4.934 7.423 0.575 0.298 0.120 20.437 0.429 0.567 -belief flower 0.111 1.440 0.333 0.108 0.596 2.612 0.083 0.000 0.071 23.278 0.125 0.183 -winner goal 0.100 1.335 0.471 0.157 1.532 1.227 0.180 0.000 0.069 23.824 0.135 0.127 -choice vein 0.111 1.440 0.333 0.108 0.596 2.612 0.066 0.000 0.056 26.517 0.125 0.183 -hymn conquest 0.077 1.073 0.250 0.049 0.596 2.612 0.056 0.228 0.048 18.730 0.087 0.130 -jail choice 0.059 0.804 0.111 0.000 0.000 0.000 0.000 0.000 0.053 28.908 0.059 0.059 -condition boy 0.100 1.335 0.182 0.000 0.000 0.000 0.000 0.000 0.077 25.684 0.100 0.100 -hole agreement 0.143 1.692 0.571 0.285 3.140 6.399 0.349 0.000 0.079 29.938 0.251 0.410 -doctor temper 0.083 1.153 0.267 0.060 0.596 2.612 0.062 0.000 0.053 21.560 0.094 0.140 -fraternity door 0.083 1.153 0.235 0.040 0.596 2.612 0.058 0.000 0.049 27.048 0.083 0.121 -task woman 0.091 1.240 0.286 0.073 0.596 2.612 0.081 0.000 0.074 16.116 0.103 0.152 -fraternity baseball 0.067 0.930 0.222 0.033 0.596 2.612 0.056 0.198 0.048 22.228 0.075 0.113 -mouse management 0.091 1.240 0.222 0.033 0.596 2.612 0.065 0.000 0.055 26.606 0.091 0.113 -task highway 0.067 0.930 0.125 0.000 0.000 0.000 0.000 0.000 0.060 18.601 0.067 0.067 -island task 0.083 1.153 0.154 0.000 0.000 0.000 0.000 0.000 0.072 16.992 0.083 0.083 -gun trick 0.125 1.558 0.588 0.246 2.334 4.982 0.260 0.000 0.070 30.449 0.194 0.303 -bath trick 0.143 1.692 0.667 0.300 3.734 7.001 0.333 0.000 0.064 30.449 0.252 0.363 -diet apple 0.100 1.335 0.400 0.138 3.337 5.761 0.307 0.498 0.062 12.592 0.190 0.287 -dentist colonel 0.077 1.073 0.462 0.091 2.334 1.540 0.215 0.153 0.055 18.001 0.123 0.105 -hole wife 0.100 1.335 0.471 0.157 1.532 1.227 0.187 0.099 0.070 23.982 0.135 0.127 -pupil president 0.167 1.846 0.632 0.367 2.334 2.612 0.316 0.195 0.090 18.265 0.252 0.220 -bath wife 0.083 1.153 0.421 0.105 1.532 1.227 0.162 0.099 0.059 23.437 0.113 0.107 -formula log 0.111 1.440 0.429 0.168 3.337 5.761 0.312 0.321 0.064 24.020 0.183 0.266 -woman fur 0.091 1.240 0.444 0.128 1.532 2.612 0.172 0.106 0.064 16.757 0.123 0.130 -camera president 0.083 1.153 0.421 0.105 1.532 1.227 0.172 0.135 0.064 24.439 0.113 0.107 -liquor band 0.100 1.335 0.308 0.089 0.802 2.612 0.092 0.278 0.065 25.951 0.113 0.166 -gun fur 0.111 1.440 0.556 0.199 2.306 2.729 0.239 0.086 0.064 26.162 0.173 0.187 -worker camera 0.100 1.335 0.471 0.157 1.532 1.227 0.202 0.157 0.076 22.080 0.135 0.127 -people gun 0.100 1.335 0.308 0.089 0.596 2.612 0.081 0.000 0.071 26.894 0.113 0.166 -princess island 0.111 1.440 0.429 0.168 1.290 0.955 0.139 0.129 0.059 13.849 0.143 0.134 -container mouse 0.200 2.028 0.750 0.447 3.258 3.625 0.388 0.123 0.089 18.573 0.341 0.360 -wagon container 0.333 2.539 0.875 0.669 5.206 6.699 0.676 0.807 0.167 8.188 0.615 0.690 -bicycle bedroom 0.111 1.440 0.556 0.199 2.306 2.729 0.244 0.202 0.065 22.623 0.173 0.187 -log bath 0.125 1.558 0.632 0.245 3.258 3.625 0.298 0.000 0.061 30.854 0.228 0.243 diff --git a/sematch/eval/word_similarity/results_original/type_ws353-sim.txt b/sematch/eval/word_similarity/results_original/type_ws353-sim.txt deleted file mode 100644 index f14f40f..0000000 --- a/sematch/eval/word_similarity/results_original/type_ws353-sim.txt +++ /dev/null @@ -1,129 +0,0 @@ -word1 word2 path lch wup li res res_graph lin lin_graph jcn jcn_graph wpath wpath_graph -tiger cat 0.500 2.944 0.966 0.819 9.841 12.278 0.894 0.132 0.301 21.137 0.900 0.939 -tiger tiger 1.000 3.638 1.000 1.000 12.268 10.819 1.000 1.000 1.000 1.000 1.000 1.000 -plane car 0.143 1.692 0.727 0.301 5.922 6.108 0.719 0.664 0.178 18.508 0.385 0.394 -train car 0.167 1.846 0.737 0.367 5.769 6.095 0.679 0.683 0.155 17.937 0.420 0.438 -doctor nurse 0.250 2.251 0.870 0.549 7.930 5.858 0.835 0.799 0.242 11.087 0.662 0.552 -professor doctor 0.143 1.692 0.545 0.301 6.394 4.280 0.704 0.565 0.157 7.587 0.410 0.302 -student professor 0.125 1.558 0.571 0.246 2.334 1.540 0.276 0.245 0.076 16.814 0.194 0.168 -wood forest 1.000 3.638 1.000 0.995 9.002 10.774 1.000 1.000 1.000 24.919 1.000 1.000 -king queen 1.000 3.638 1.000 1.000 9.568 14.581 1.000 0.181 1.000 20.959 1.000 1.000 -bishop rabbi 0.200 2.028 0.545 0.449 6.962 5.572 0.666 0.530 0.125 10.592 0.542 0.455 -vodka gin 0.333 2.539 0.909 0.670 8.856 12.229 0.000 0.896 0.000 3.831 0.783 0.884 -vodka brandy 0.333 2.539 0.909 0.670 8.856 12.229 0.000 0.920 0.000 3.138 0.783 0.884 -car automobile 1.000 3.638 1.000 1.000 7.591 7.386 1.000 1.000 1.000 14.869 1.000 1.000 -gem jewel 1.000 3.638 1.000 1.000 12.068 12.876 1.000 1.000 1.000 25.681 1.000 1.000 -journey voyage 0.500 2.944 0.952 0.819 6.826 9.326 0.778 0.833 0.204 5.911 0.821 0.889 -coast shore 0.500 2.944 0.909 0.805 9.416 11.560 0.963 0.992 0.582 1.187 0.891 0.930 -magician wizard 1.000 3.638 0.667 1.000 11.981 14.175 1.000 1.000 1.000 23.732 1.000 1.000 -food fruit 0.100 1.335 0.400 0.138 1.593 3.724 0.161 0.108 0.084 17.002 0.137 0.203 -bird cock 0.500 2.944 0.952 0.819 7.678 9.339 0.774 0.108 0.182 26.511 0.847 0.889 -money currency 0.500 2.944 0.933 0.818 6.445 9.721 0.864 0.878 0.336 3.672 0.808 0.897 -tiger jaguar 0.333 2.539 0.933 0.670 9.841 12.278 0.797 0.120 0.167 22.747 0.818 0.886 -tiger feline 0.333 2.539 0.929 0.670 8.663 10.867 0.832 0.137 0.222 19.726 0.776 0.850 -tiger carnivore 0.250 2.251 0.889 0.549 7.912 9.490 0.788 0.146 0.190 18.349 0.661 0.735 -tiger mammal 0.167 1.846 0.800 0.368 5.406 7.040 0.615 0.166 0.129 15.899 0.401 0.490 -tiger animal 0.250 2.251 0.800 0.546 4.946 4.834 0.578 0.189 0.122 13.693 0.354 0.317 -tiger organism 0.333 2.539 0.857 0.667 2.224 1.480 0.309 0.241 0.091 10.339 0.451 0.410 -tiger fauna 0.250 2.251 0.800 0.546 4.946 4.834 0.578 0.189 0.122 13.693 0.354 0.317 -planet star 0.333 2.539 0.857 0.667 6.932 8.564 0.874 0.894 0.334 25.979 0.701 0.772 -cup tableware 0.333 2.539 0.889 0.670 7.490 8.640 0.840 0.835 0.260 4.414 0.727 0.775 -cup artifact 0.250 2.251 0.769 0.540 2.306 2.729 0.365 0.369 0.111 10.326 0.358 0.380 -cup object 0.167 1.846 0.545 0.307 2.620 4.436 0.261 0.147 0.100 12.100 0.211 0.212 -jaguar cat 0.500 2.944 0.966 0.819 9.841 12.278 0.880 0.938 0.272 4.790 0.900 0.939 -jaguar car 0.059 0.804 0.333 0.039 1.532 1.227 0.152 0.115 0.055 24.816 0.081 0.076 -announcement news 0.200 2.028 0.667 0.425 3.848 6.513 0.490 0.646 0.111 20.821 0.371 0.517 -doctor personnel 0.083 1.153 0.267 0.060 0.596 2.612 0.076 0.000 0.066 15.993 0.094 0.140 -hospital infrastructure 0.083 1.153 0.267 0.060 0.596 2.612 0.061 0.000 0.052 22.811 0.094 0.140 -life death 0.333 2.539 0.857 0.667 7.411 7.001 0.888 0.217 0.349 23.327 0.723 0.582 -travel activity 0.167 1.846 0.667 0.362 2.332 4.982 0.500 0.679 0.177 28.908 0.252 0.378 -type kind 0.500 2.944 0.947 0.819 6.421 6.450 0.896 0.283 0.402 14.241 0.807 0.808 -street place 0.167 1.846 0.615 0.348 3.140 6.399 0.366 0.122 0.084 20.306 0.287 0.455 -street avenue 0.500 2.944 0.947 0.819 8.670 8.139 0.913 0.716 0.377 7.442 0.874 0.860 -street block 0.167 1.846 0.667 0.362 2.855 5.951 0.306 0.312 0.087 14.947 0.251 0.350 -cell phone 0.333 2.539 0.900 0.670 8.723 10.943 0.959 0.999 0.575 27.763 0.778 0.842 -dividend payment 0.333 2.539 0.889 0.670 6.737 13.664 0.726 0.604 0.164 12.464 0.692 0.913 -calculation computation 1.000 3.638 1.000 1.000 7.760 10.956 1.000 1.000 1.000 1.000 1.000 1.000 -phone equipment 0.333 2.539 0.875 0.669 5.268 9.016 0.753 0.919 0.224 22.892 0.618 0.789 -liquid water 0.500 2.944 0.933 0.818 8.115 14.581 0.994 1.000 0.916 18.792 0.859 0.963 -seafood food 0.500 2.944 0.909 0.805 6.109 8.084 0.790 0.860 0.235 7.581 0.796 0.859 -seafood lobster 0.333 2.539 0.857 0.667 9.360 10.720 0.809 0.962 0.185 21.767 0.801 0.845 -lobster food 0.250 2.251 0.769 0.540 6.109 8.084 0.615 0.823 0.115 19.131 0.566 0.669 -lobster wine 0.091 1.240 0.375 0.113 3.337 5.761 0.297 0.470 0.059 24.019 0.174 0.266 -championship tournament 0.333 2.539 0.857 0.667 6.609 5.809 0.633 0.599 0.115 8.792 0.686 0.646 -man woman 0.333 2.539 0.706 0.670 4.910 4.037 0.787 0.505 0.273 18.995 0.599 0.552 -man governor 0.200 2.028 0.667 0.449 3.258 3.625 0.402 0.284 0.093 19.424 0.296 0.261 -opera performance 0.100 1.335 0.308 0.089 0.802 2.612 0.084 0.243 0.057 25.844 0.117 0.152 -glass metal 0.167 1.846 0.545 0.307 3.337 5.761 0.454 0.081 0.111 22.755 0.296 0.420 -museum theater 0.143 1.692 0.625 0.296 2.306 2.729 0.248 0.355 0.067 10.918 0.218 0.235 -monk oracle 0.125 1.558 0.571 0.246 2.334 1.540 0.226 0.164 0.059 16.711 0.194 0.168 -cup food 0.167 1.846 0.667 0.362 5.279 7.383 0.672 0.096 0.163 19.280 0.394 0.510 -journal association 0.143 1.692 0.400 0.162 0.802 2.612 0.081 0.323 0.059 20.482 0.160 0.230 -street children 0.100 1.335 0.471 0.157 1.532 1.227 0.198 0.151 0.074 14.889 0.135 0.127 -car flight 0.125 1.558 0.588 0.243 2.306 2.729 0.256 0.000 0.070 27.559 0.193 0.208 -situation conclusion 0.143 1.692 0.625 0.296 2.332 4.982 0.346 0.476 0.102 20.213 0.219 0.336 -word similarity 0.111 1.440 0.455 0.108 3.292 5.771 0.335 0.230 0.071 21.823 0.148 0.232 -peace plan 0.100 1.335 0.308 0.089 0.596 2.612 0.076 0.249 0.065 18.944 0.113 0.166 -consumer energy 0.100 1.335 0.308 0.089 0.802 0.929 0.114 0.090 0.074 21.267 0.117 0.120 -ministry culture 0.125 1.558 0.533 0.233 3.336 4.982 0.314 0.313 0.064 17.387 0.231 0.235 -investigation effort 0.250 2.251 0.824 0.548 4.806 8.647 0.693 0.754 0.190 20.412 0.493 0.697 -image surface 0.200 2.028 0.714 0.442 3.292 5.771 0.378 0.232 0.099 28.369 0.295 0.398 -life term 0.333 2.539 0.857 0.667 6.494 14.581 0.905 0.199 0.423 24.020 0.680 0.928 -start match 0.143 1.692 0.571 0.285 2.691 7.001 0.307 0.406 0.078 26.811 0.233 0.443 -computer news 0.091 1.240 0.167 0.000 0.000 0.000 0.000 0.000 0.057 24.070 0.091 0.091 -board recommendation 0.083 1.153 0.267 0.060 0.596 2.612 0.068 0.216 0.057 19.995 0.094 0.140 -observation architecture 0.167 1.846 0.706 0.366 3.144 5.519 0.295 0.000 0.067 23.688 0.287 0.407 -coast hill 0.200 2.028 0.667 0.425 5.885 5.283 0.599 0.521 0.113 26.112 0.482 0.448 -deployment departure 0.200 2.028 0.714 0.442 2.332 4.982 0.212 0.369 0.054 18.029 0.296 0.432 -benchmark index 0.250 2.251 0.769 0.540 6.110 8.579 0.514 0.736 0.080 23.065 0.566 0.693 -attempt peace 0.100 1.335 0.308 0.089 0.596 2.612 0.077 0.221 0.065 19.389 0.113 0.166 -start year 0.167 1.846 0.545 0.307 2.691 7.001 0.385 0.272 0.104 22.228 0.267 0.488 -development issue 0.250 2.251 0.750 0.458 4.089 9.717 0.567 0.309 0.138 26.311 0.454 0.745 -theater history 0.143 1.692 0.500 0.251 1.593 3.724 0.182 0.000 0.065 20.069 0.192 0.277 -profit warning 0.091 1.240 0.286 0.073 0.596 2.612 0.074 0.194 0.063 22.739 0.103 0.152 -peace atmosphere 0.200 2.028 0.714 0.442 4.235 6.780 0.501 0.228 0.106 26.259 0.391 0.532 -morality marriage 0.167 1.846 0.545 0.307 2.298 5.951 0.306 0.227 0.088 18.793 0.250 0.430 -minority peace 0.167 1.846 0.615 0.348 3.140 6.399 0.303 0.222 0.065 19.289 0.287 0.455 -atmosphere landscape 0.111 1.440 0.500 0.191 3.012 2.612 0.299 0.074 0.066 25.094 0.197 0.176 -report gain 0.125 1.558 0.364 0.132 0.596 2.612 0.084 0.212 0.071 23.145 0.140 0.204 -music project 0.250 2.251 0.800 0.546 3.144 5.519 0.432 0.353 0.108 10.556 0.402 0.533 -seven series 0.111 1.440 0.375 0.113 3.037 5.511 0.316 0.235 0.071 18.821 0.165 0.255 -experience music 0.167 1.846 0.615 0.348 2.645 5.359 0.329 0.251 0.086 16.582 0.240 0.324 -school center 0.333 2.539 0.875 0.669 5.715 5.359 0.669 0.447 0.150 21.087 0.642 0.591 -five month 0.125 1.558 0.462 0.206 2.691 7.001 0.374 0.504 0.100 16.906 0.207 0.405 -announcement production 0.143 1.692 0.500 0.251 2.620 4.436 0.301 0.504 0.076 19.581 0.230 0.310 -money operation 0.111 1.440 0.333 0.108 0.596 2.612 0.096 0.251 0.082 21.081 0.125 0.183 -delay news 0.125 1.558 0.533 0.233 2.036 3.923 0.213 0.218 0.062 20.494 0.184 0.255 -practice institution 0.333 2.539 0.833 0.659 7.355 13.077 0.871 0.342 0.315 24.597 0.721 0.902 -coast forest 0.167 1.846 0.545 0.307 1.290 2.612 0.131 0.077 0.055 24.032 0.211 0.198 -shore woodland 0.200 2.028 0.600 0.375 1.290 0.955 0.136 0.077 0.057 23.845 0.250 0.236 -drink car 0.091 1.240 0.286 0.073 0.802 0.929 0.112 0.117 0.073 25.074 0.107 0.110 -president medal 0.077 1.073 0.235 0.040 0.596 2.612 0.063 0.000 0.054 24.255 0.081 0.121 -prejudice recognition 0.167 1.846 0.667 0.362 6.039 10.659 0.555 0.212 0.094 21.527 0.435 0.683 -viewer serial 0.091 1.240 0.500 0.133 2.306 2.729 0.212 0.000 0.061 20.761 0.143 0.155 -peace insurance 0.250 2.251 0.824 0.548 8.459 13.887 0.842 0.189 0.256 23.432 0.688 0.881 -media gain 0.143 1.692 0.500 0.251 3.037 5.951 0.369 0.449 0.088 25.817 0.220 0.386 -announcement effort 0.100 1.335 0.308 0.089 0.596 2.612 0.085 0.276 0.072 23.684 0.113 0.166 -line insurance 0.200 2.028 0.714 0.442 5.176 10.997 0.559 0.773 0.121 30.854 0.442 0.744 -drink mother 0.167 1.846 0.615 0.348 4.615 6.858 0.595 0.096 0.137 23.545 0.359 0.480 -opera industry 0.091 1.240 0.286 0.073 0.596 2.612 0.065 0.249 0.055 22.317 0.103 0.152 -volunteer motto 0.083 1.153 0.154 0.000 0.000 0.000 0.000 0.000 0.047 24.139 0.083 0.083 -cup article 0.200 2.028 0.750 0.447 7.332 8.640 0.830 0.835 0.250 4.414 0.562 0.632 -sign recess 0.200 2.028 0.750 0.447 3.979 4.386 0.438 0.287 0.089 27.504 0.378 0.399 -problem airport 0.071 0.999 0.133 0.000 0.000 0.000 0.000 0.000 0.056 19.566 0.071 0.071 -direction combination 0.143 1.692 0.667 0.300 3.144 5.519 0.334 0.384 0.088 29.245 0.252 0.363 -glass magician 0.125 1.558 0.471 0.206 2.283 1.532 0.214 0.125 0.056 23.258 0.192 0.167 -cemetery woodland 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.087 0.052 21.050 0.143 0.134 -possibility girl 0.091 1.240 0.167 0.000 0.000 0.000 0.000 0.000 0.068 25.571 0.091 0.091 -cup substance 0.143 1.692 0.571 0.285 4.615 6.858 0.614 0.102 0.147 19.568 0.318 0.435 -forest graveyard 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.087 0.052 21.050 0.143 0.134 -stock egg 0.143 1.692 0.625 0.296 5.279 7.383 0.545 0.000 0.102 28.839 0.351 0.464 -month hotel 0.083 1.153 0.154 0.000 0.000 0.000 0.000 0.000 0.063 21.194 0.083 0.083 -energy secretary 0.091 1.240 0.286 0.073 0.802 0.929 0.102 0.093 0.066 25.770 0.107 0.110 -stock phone 0.143 1.692 0.700 0.301 4.076 6.754 0.414 0.415 0.093 29.245 0.293 0.429 -holy sex 0.100 1.335 0.308 0.089 0.802 0.929 0.091 0.071 0.059 25.440 0.117 0.120 -stock CD 0.167 1.846 0.737 0.367 4.076 6.754 0.372 0.200 0.068 24.713 0.332 0.474 -stock life 0.167 1.846 0.615 0.348 3.140 6.399 0.368 0.232 0.085 23.104 0.224 0.373 -stock jaguar 0.167 1.846 0.815 0.368 5.414 7.651 0.494 0.000 0.083 28.552 0.401 0.524 -monk slave 0.200 2.028 0.667 0.449 2.334 1.540 0.254 0.174 0.068 15.571 0.296 0.261 -sugar approach 0.091 1.240 0.286 0.073 0.802 2.612 0.087 0.215 0.056 27.373 0.107 0.140 diff --git a/sematch/eval/word_similarity/results_original/type_ws353.txt b/sematch/eval/word_similarity/results_original/type_ws353.txt deleted file mode 100644 index ba97fff..0000000 --- a/sematch/eval/word_similarity/results_original/type_ws353.txt +++ /dev/null @@ -1,212 +0,0 @@ -word1 word2 path lch wup li res res_graph lin lin_graph jcn jcn_graph wpath wpath_graph -love sex 0.500 2.944 0.923 0.815 6.690 13.664 0.852 0.000 0.300 26.994 0.817 0.955 -tiger cat 0.500 2.944 0.966 0.819 9.841 12.278 0.894 0.132 0.301 21.137 0.900 0.939 -tiger tiger 1.000 3.638 1.000 1.000 12.268 10.819 1.000 1.000 1.000 1.000 1.000 1.000 -book paper 0.333 2.539 0.875 0.669 5.455 5.519 0.674 0.406 0.159 21.081 0.628 0.558 -computer keyboard 0.250 2.251 0.824 0.548 4.076 6.754 0.374 0.634 0.068 22.503 0.453 0.601 -computer internet 0.125 1.558 0.632 0.245 3.258 3.625 0.330 0.312 0.070 24.449 0.228 0.243 -plane car 0.143 1.692 0.727 0.301 5.922 6.108 0.719 0.664 0.178 18.508 0.385 0.394 -train car 0.167 1.846 0.737 0.367 5.769 6.095 0.679 0.683 0.155 17.937 0.420 0.438 -telephone communication 0.091 1.240 0.167 0.000 0.000 0.000 0.000 0.000 0.081 22.407 0.091 0.091 -drug abuse 0.100 1.335 0.182 0.000 0.000 0.000 0.000 0.000 0.063 22.366 0.100 0.100 -doctor nurse 0.250 2.251 0.870 0.549 7.930 5.858 0.835 0.799 0.242 11.087 0.662 0.552 -professor doctor 0.143 1.692 0.545 0.301 6.394 4.280 0.704 0.565 0.157 7.587 0.410 0.302 -student professor 0.125 1.558 0.571 0.246 2.334 1.540 0.276 0.245 0.076 16.814 0.194 0.168 -company stock 0.167 1.846 0.615 0.348 3.336 6.399 0.483 0.442 0.123 20.369 0.296 0.343 -stock market 0.143 1.692 0.556 0.251 3.336 3.440 0.455 0.233 0.111 26.006 0.240 0.260 -stock phone 0.143 1.692 0.700 0.301 4.076 6.754 0.414 0.415 0.093 29.245 0.293 0.429 -stock CD 0.167 1.846 0.737 0.367 4.076 6.754 0.372 0.200 0.068 24.713 0.332 0.474 -stock jaguar 0.167 1.846 0.815 0.368 5.414 7.651 0.494 0.000 0.083 28.552 0.401 0.524 -stock egg 0.143 1.692 0.625 0.296 5.279 7.383 0.545 0.000 0.102 28.839 0.351 0.464 -stock life 0.167 1.846 0.615 0.348 3.140 6.399 0.368 0.232 0.085 23.104 0.224 0.373 -book library 0.333 2.539 0.800 0.635 5.053 7.853 0.537 0.381 0.103 23.320 0.607 0.743 -bank money 0.143 1.692 0.571 0.285 4.470 8.737 0.483 0.259 0.095 26.711 0.311 0.539 -wood forest 1.000 3.638 1.000 0.995 9.002 10.774 1.000 1.000 1.000 24.919 1.000 1.000 -king queen 1.000 3.638 1.000 1.000 9.568 14.581 1.000 0.181 1.000 20.959 1.000 1.000 -bishop rabbi 0.200 2.028 0.545 0.449 6.962 5.572 0.666 0.530 0.125 10.592 0.542 0.455 -holy sex 0.100 1.335 0.308 0.089 0.802 0.929 0.091 0.071 0.059 25.440 0.117 0.120 -law lawyer 0.091 1.240 0.167 0.000 0.000 0.000 0.000 0.000 0.058 20.727 0.091 0.091 -movie star 0.125 1.558 0.533 0.233 1.593 3.724 0.189 0.274 0.071 14.828 0.167 0.173 -movie critic 0.091 1.240 0.444 0.128 1.532 1.227 0.180 0.207 0.067 10.901 0.123 0.116 -movie theater 0.143 1.692 0.625 0.296 2.306 3.923 0.293 0.426 0.083 8.363 0.218 0.286 -vodka gin 0.333 2.539 0.909 0.670 8.856 12.229 0.000 0.896 0.000 3.831 0.783 0.884 -vodka brandy 0.333 2.539 0.909 0.670 8.856 12.229 0.000 0.920 0.000 3.138 0.783 0.884 -drink car 0.091 1.240 0.286 0.073 0.802 0.929 0.112 0.117 0.073 25.074 0.107 0.110 -baby mother 0.200 2.028 0.632 0.449 6.257 7.095 0.684 0.567 0.147 11.839 0.502 0.481 -drink mother 0.167 1.846 0.615 0.348 4.615 6.858 0.595 0.096 0.137 23.545 0.359 0.480 -car automobile 1.000 3.638 1.000 1.000 7.591 7.386 1.000 1.000 1.000 14.869 1.000 1.000 -gem jewel 1.000 3.638 1.000 1.000 12.068 12.876 1.000 1.000 1.000 25.681 1.000 1.000 -journey voyage 0.500 2.944 0.952 0.819 6.826 9.326 0.778 0.833 0.204 5.911 0.821 0.889 -coast shore 0.500 2.944 0.909 0.805 9.416 11.560 0.963 0.992 0.582 1.187 0.891 0.930 -magician wizard 1.000 3.638 0.667 1.000 11.981 14.175 1.000 1.000 1.000 23.732 1.000 1.000 -food fruit 0.100 1.335 0.400 0.138 1.593 3.724 0.161 0.108 0.084 17.002 0.137 0.203 -bird cock 0.500 2.944 0.952 0.819 7.678 9.339 0.774 0.108 0.182 26.511 0.847 0.889 -tool implement 0.500 2.944 0.933 0.818 5.877 10.564 0.947 0.991 0.604 1.199 0.788 0.914 -brother monk 0.500 2.944 0.952 0.819 9.262 8.392 0.986 0.165 0.797 18.254 0.888 0.867 -journey car 0.056 0.747 0.105 0.000 0.000 0.000 0.000 0.000 0.065 22.709 0.056 0.056 -monk oracle 0.125 1.558 0.571 0.246 2.334 1.540 0.226 0.164 0.059 16.711 0.194 0.168 -cemetery woodland 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.087 0.052 21.050 0.143 0.134 -coast hill 0.200 2.028 0.667 0.425 5.885 5.283 0.599 0.521 0.113 26.112 0.482 0.448 -forest graveyard 0.111 1.440 0.429 0.168 1.290 0.955 0.123 0.087 0.052 21.050 0.143 0.134 -shore woodland 0.200 2.028 0.600 0.375 1.290 0.955 0.136 0.077 0.057 23.845 0.250 0.236 -monk slave 0.200 2.028 0.667 0.449 2.334 1.540 0.254 0.174 0.068 15.571 0.296 0.261 -coast forest 0.167 1.846 0.545 0.307 1.290 2.612 0.131 0.077 0.055 24.032 0.211 0.198 -glass magician 0.125 1.558 0.471 0.206 2.283 1.532 0.214 0.125 0.056 23.258 0.192 0.167 -money currency 0.500 2.944 0.933 0.818 6.445 9.721 0.864 0.878 0.336 3.672 0.808 0.897 -money wealth 0.500 2.944 0.923 0.815 8.660 8.737 0.996 0.213 0.936 20.334 0.874 0.539 -money property 0.333 2.539 0.833 0.659 6.897 9.117 0.883 0.252 0.353 23.569 0.700 0.793 -money possession 0.250 2.251 0.727 0.520 4.470 8.737 0.677 0.249 0.190 20.445 0.475 0.701 -money bank 0.143 1.692 0.571 0.285 4.470 8.737 0.483 0.259 0.095 26.711 0.311 0.539 -money deposit 0.333 2.539 0.875 0.669 8.042 12.229 0.812 0.000 0.212 25.795 0.751 0.884 -money withdrawal 0.091 1.240 0.286 0.073 0.596 2.612 0.067 0.190 0.057 23.278 0.103 0.152 -money operation 0.111 1.440 0.333 0.108 0.596 2.612 0.096 0.251 0.082 21.081 0.125 0.183 -tiger jaguar 0.333 2.539 0.933 0.670 9.841 12.278 0.797 0.120 0.167 22.747 0.818 0.886 -tiger feline 0.333 2.539 0.929 0.670 8.663 10.867 0.832 0.137 0.222 19.726 0.776 0.850 -tiger carnivore 0.250 2.251 0.889 0.549 7.912 9.490 0.788 0.146 0.190 18.349 0.661 0.735 -tiger mammal 0.167 1.846 0.800 0.368 5.406 7.040 0.615 0.166 0.129 15.899 0.401 0.490 -tiger animal 0.250 2.251 0.800 0.546 4.946 4.834 0.578 0.189 0.122 13.693 0.354 0.317 -tiger organism 0.333 2.539 0.857 0.667 2.224 1.480 0.309 0.241 0.091 10.339 0.451 0.410 -tiger fauna 0.250 2.251 0.800 0.546 4.946 4.834 0.578 0.189 0.122 13.693 0.354 0.317 -tiger zoo 0.125 1.558 0.533 0.233 1.532 1.227 0.126 0.111 0.045 20.727 0.167 0.158 -planet star 0.333 2.539 0.857 0.667 6.932 8.564 0.874 0.894 0.334 25.979 0.701 0.772 -planet constellation 0.250 2.251 0.769 0.540 4.940 8.119 0.592 0.000 0.128 22.303 0.501 0.671 -planet galaxy 0.125 1.558 0.632 0.245 2.224 1.480 0.197 0.000 0.057 25.286 0.190 0.166 -planet space 0.143 1.692 0.533 0.251 1.532 1.227 0.191 0.105 0.084 24.370 0.182 0.171 -planet astronomer 0.167 1.846 0.632 0.367 2.334 1.540 0.242 0.138 0.070 16.371 0.252 0.220 -cup coffee 0.167 1.846 0.762 0.368 6.693 9.625 0.651 0.068 0.122 26.470 0.471 0.631 -cup tableware 0.333 2.539 0.889 0.670 7.490 8.640 0.840 0.835 0.260 4.414 0.727 0.775 -cup article 0.200 2.028 0.750 0.447 7.332 8.640 0.830 0.835 0.250 4.414 0.562 0.632 -cup artifact 0.250 2.251 0.769 0.540 2.306 2.729 0.365 0.369 0.111 10.326 0.358 0.380 -cup object 0.167 1.846 0.545 0.307 2.620 4.436 0.261 0.147 0.100 12.100 0.211 0.212 -cup drink 0.200 2.028 0.800 0.449 6.693 10.132 0.782 0.090 0.211 24.745 0.527 0.657 -cup food 0.167 1.846 0.667 0.362 5.279 7.383 0.672 0.096 0.163 19.280 0.394 0.510 -cup substance 0.143 1.692 0.571 0.285 4.615 6.858 0.614 0.102 0.147 19.568 0.318 0.435 -cup liquid 0.167 1.846 0.737 0.367 6.444 8.429 0.764 0.091 0.201 25.777 0.457 0.567 -jaguar cat 0.500 2.944 0.966 0.819 9.841 12.278 0.880 0.938 0.272 4.790 0.900 0.939 -jaguar car 0.059 0.804 0.333 0.039 1.532 1.227 0.152 0.115 0.055 24.816 0.081 0.076 -energy secretary 0.091 1.240 0.286 0.073 0.802 0.929 0.102 0.093 0.066 25.770 0.107 0.110 -energy laboratory 0.100 1.335 0.308 0.089 0.802 0.929 0.093 0.074 0.060 25.659 0.117 0.120 -computer laboratory 0.111 1.440 0.429 0.168 1.290 0.955 0.134 0.094 0.056 22.049 0.143 0.134 -weapon secret 0.091 1.240 0.286 0.073 0.596 2.612 0.067 0.000 0.063 25.021 0.103 0.152 -investigation effort 0.250 2.251 0.824 0.548 4.806 8.647 0.693 0.754 0.190 20.412 0.493 0.697 -news report 0.500 2.944 0.923 0.815 7.743 12.441 0.989 1.000 0.849 20.976 0.849 0.941 -canyon landscape 0.083 1.153 0.353 0.092 1.290 0.955 0.114 0.085 0.047 22.372 0.108 0.101 -image surface 0.200 2.028 0.714 0.442 3.292 5.771 0.378 0.232 0.099 28.369 0.295 0.398 -discovery space 0.143 1.692 0.400 0.162 0.596 2.612 0.089 0.251 0.076 23.158 0.160 0.230 -sign recess 0.200 2.028 0.750 0.447 3.979 4.386 0.438 0.287 0.089 27.504 0.378 0.399 -computer news 0.091 1.240 0.167 0.000 0.000 0.000 0.000 0.000 0.057 24.070 0.091 0.091 -territory surface 0.167 1.846 0.615 0.348 3.292 5.771 0.557 0.304 0.173 12.353 0.281 0.355 -atmosphere landscape 0.111 1.440 0.500 0.191 3.012 2.612 0.299 0.074 0.066 25.094 0.197 0.176 -president medal 0.077 1.073 0.235 0.040 0.596 2.612 0.063 0.000 0.054 24.255 0.081 0.121 -war troops 0.100 1.335 0.308 0.089 0.596 2.612 0.071 0.229 0.060 18.585 0.113 0.166 -record number 0.500 2.944 0.909 0.805 5.819 9.297 0.796 0.771 0.251 29.756 0.726 0.868 -theater history 0.143 1.692 0.500 0.251 1.593 3.724 0.182 0.000 0.065 20.069 0.192 0.277 -volunteer motto 0.083 1.153 0.154 0.000 0.000 0.000 0.000 0.000 0.047 24.139 0.083 0.083 -prejudice recognition 0.167 1.846 0.667 0.362 6.039 10.659 0.555 0.212 0.094 21.527 0.435 0.683 -delay news 0.125 1.558 0.533 0.233 2.036 3.923 0.213 0.218 0.062 20.494 0.184 0.255 -minister party 0.167 1.846 0.632 0.367 2.334 3.923 0.259 0.185 0.070 23.301 0.252 0.220 -peace plan 0.100 1.335 0.308 0.089 0.596 2.612 0.076 0.249 0.065 18.944 0.113 0.166 -minority peace 0.167 1.846 0.615 0.348 3.140 6.399 0.303 0.222 0.065 19.289 0.287 0.455 -attempt peace 0.100 1.335 0.308 0.089 0.596 2.612 0.077 0.221 0.065 19.389 0.113 0.166 -government crisis 0.143 1.692 0.571 0.285 2.036 3.923 0.236 0.240 0.070 19.501 0.208 0.286 -deployment departure 0.200 2.028 0.714 0.442 2.332 4.982 0.212 0.369 0.054 18.029 0.296 0.432 -deployment withdrawal 0.200 2.028 0.778 0.449 8.772 11.808 0.650 0.344 0.096 19.975 0.639 0.777 -energy crisis 0.143 1.692 0.625 0.296 4.235 6.780 0.443 0.534 0.086 26.865 0.300 0.431 -announcement news 0.200 2.028 0.667 0.425 3.848 6.513 0.490 0.646 0.111 20.821 0.371 0.517 -announcement effort 0.100 1.335 0.308 0.089 0.596 2.612 0.085 0.276 0.072 23.684 0.113 0.166 -stroke hospital 0.091 1.240 0.400 0.086 1.532 2.612 0.155 0.000 0.063 22.588 0.105 0.152 -disability death 0.125 1.558 0.533 0.233 3.140 6.399 0.336 0.197 0.074 23.838 0.224 0.373 -victim emergency 0.100 1.335 0.444 0.128 1.532 1.227 0.159 0.000 0.058 18.907 0.123 0.116 -journal association 0.143 1.692 0.400 0.162 0.802 2.612 0.081 0.323 0.059 20.482 0.160 0.230 -doctor personnel 0.083 1.153 0.267 0.060 0.596 2.612 0.076 0.000 0.066 15.993 0.094 0.140 -school center 0.333 2.539 0.875 0.669 5.715 5.359 0.669 0.447 0.150 21.087 0.642 0.591 -hospital infrastructure 0.083 1.153 0.267 0.060 0.596 2.612 0.061 0.000 0.052 22.811 0.094 0.140 -death inmate 0.091 1.240 0.286 0.073 0.802 0.929 0.080 0.000 0.051 25.349 0.107 0.110 -lawyer evidence 0.091 1.240 0.167 0.000 0.000 0.000 0.000 0.000 0.066 21.826 0.091 0.091 -life death 0.333 2.539 0.857 0.667 7.411 7.001 0.888 0.217 0.349 23.327 0.723 0.582 -life term 0.333 2.539 0.857 0.667 6.494 14.581 0.905 0.199 0.423 24.020 0.680 0.928 -word similarity 0.111 1.440 0.455 0.108 3.292 5.771 0.335 0.230 0.071 21.823 0.148 0.232 -board recommendation 0.083 1.153 0.267 0.060 0.596 2.612 0.068 0.216 0.057 19.995 0.094 0.140 -peace atmosphere 0.200 2.028 0.714 0.442 4.235 6.780 0.501 0.228 0.106 26.259 0.391 0.532 -peace insurance 0.250 2.251 0.824 0.548 8.459 13.887 0.842 0.189 0.256 23.432 0.688 0.881 -travel activity 0.167 1.846 0.667 0.362 2.332 4.982 0.500 0.679 0.177 28.908 0.252 0.378 -consumer energy 0.100 1.335 0.308 0.089 0.802 0.929 0.114 0.090 0.074 21.267 0.117 0.120 -problem airport 0.071 0.999 0.133 0.000 0.000 0.000 0.000 0.000 0.056 19.566 0.071 0.071 -car flight 0.125 1.558 0.588 0.243 2.306 2.729 0.256 0.000 0.070 27.559 0.193 0.208 -credit card 0.167 1.846 0.615 0.348 4.004 6.513 0.429 0.430 0.110 23.550 0.321 0.461 -credit information 0.250 2.251 0.727 0.520 4.934 7.423 0.551 0.675 0.125 18.471 0.440 0.588 -hotel reservation 0.091 1.240 0.375 0.113 1.290 0.955 0.141 0.101 0.060 17.980 0.118 0.110 -grocery money 0.071 0.999 0.133 0.000 0.000 0.000 0.000 0.000 0.054 24.571 0.071 0.071 -month hotel 0.083 1.153 0.154 0.000 0.000 0.000 0.000 0.000 0.063 21.194 0.083 0.083 -type kind 0.500 2.944 0.947 0.819 6.421 6.450 0.896 0.283 0.402 14.241 0.807 0.808 -situation conclusion 0.143 1.692 0.625 0.296 2.332 4.982 0.346 0.476 0.102 20.213 0.219 0.336 -direction combination 0.143 1.692 0.667 0.300 3.144 5.519 0.334 0.384 0.088 29.245 0.252 0.363 -street place 0.167 1.846 0.615 0.348 3.140 6.399 0.366 0.122 0.084 20.306 0.287 0.455 -street avenue 0.500 2.944 0.947 0.819 8.670 8.139 0.913 0.716 0.377 7.442 0.874 0.860 -street block 0.167 1.846 0.667 0.362 2.855 5.951 0.306 0.312 0.087 14.947 0.251 0.350 -street children 0.100 1.335 0.471 0.157 1.532 1.227 0.198 0.151 0.074 14.889 0.135 0.127 -listing category 0.111 1.440 0.375 0.113 1.593 3.724 0.189 0.372 0.076 13.100 0.125 0.187 -cell phone 0.333 2.539 0.900 0.670 8.723 10.943 0.959 0.999 0.575 27.763 0.778 0.842 -benchmark index 0.250 2.251 0.769 0.540 6.110 8.579 0.514 0.736 0.080 23.065 0.566 0.693 -media gain 0.143 1.692 0.500 0.251 3.037 5.951 0.369 0.449 0.088 25.817 0.220 0.386 -dividend payment 0.333 2.539 0.889 0.670 6.737 13.664 0.726 0.604 0.164 12.464 0.692 0.913 -dividend calculation 0.091 1.240 0.286 0.073 0.596 2.612 0.062 0.199 0.052 22.005 0.103 0.152 -calculation computation 1.000 3.638 1.000 1.000 7.760 10.956 1.000 1.000 1.000 1.000 1.000 1.000 -currency market 0.100 1.335 0.308 0.089 0.596 2.612 0.080 0.245 0.068 22.063 0.113 0.166 -oil stock 0.111 1.440 0.571 0.199 5.118 9.717 0.524 0.419 0.097 19.936 0.259 0.493 -announcement production 0.143 1.692 0.500 0.251 2.620 4.436 0.301 0.504 0.076 19.581 0.230 0.310 -announcement warning 0.167 1.846 0.615 0.348 3.848 6.513 0.457 0.251 0.099 22.180 0.321 0.461 -profit warning 0.091 1.240 0.286 0.073 0.596 2.612 0.074 0.194 0.063 22.739 0.103 0.152 -computer software 0.077 1.073 0.143 0.000 0.000 0.000 0.000 0.000 0.060 18.481 0.077 0.077 -phone equipment 0.333 2.539 0.875 0.669 5.268 9.016 0.753 0.919 0.224 22.892 0.618 0.789 -equipment maker 0.111 1.440 0.500 0.191 1.532 1.227 0.234 0.161 0.091 17.148 0.150 0.141 -five month 0.125 1.558 0.462 0.206 2.691 7.001 0.374 0.504 0.100 16.906 0.207 0.405 -report gain 0.125 1.558 0.364 0.132 0.596 2.612 0.084 0.212 0.071 23.145 0.140 0.204 -liquid water 0.500 2.944 0.933 0.818 8.115 14.581 0.994 1.000 0.916 18.792 0.859 0.963 -baseball season 0.067 0.930 0.222 0.033 0.596 2.612 0.069 0.205 0.059 22.739 0.075 0.113 -game victory 0.143 1.692 0.571 0.285 2.036 3.923 0.264 0.508 0.081 25.574 0.208 0.286 -game team 0.100 1.335 0.308 0.089 0.596 2.612 0.084 0.418 0.071 22.610 0.113 0.166 -game series 0.333 2.539 0.857 0.667 6.609 5.809 0.699 0.537 0.150 26.530 0.686 0.646 -seven series 0.111 1.440 0.375 0.113 3.037 5.511 0.316 0.235 0.071 18.821 0.165 0.255 -seafood sea 0.125 1.558 0.364 0.132 0.802 0.929 0.085 0.083 0.055 21.446 0.146 0.150 -seafood food 0.500 2.944 0.909 0.805 6.109 8.084 0.790 0.860 0.235 7.581 0.796 0.859 -seafood lobster 0.333 2.539 0.857 0.667 9.360 10.720 0.809 0.962 0.185 21.767 0.801 0.845 -lobster food 0.250 2.251 0.769 0.540 6.109 8.084 0.615 0.823 0.115 19.131 0.566 0.669 -lobster wine 0.091 1.240 0.375 0.113 3.337 5.761 0.297 0.470 0.059 24.019 0.174 0.266 -food preparation 0.143 1.692 0.571 0.285 3.337 5.761 0.461 0.587 0.114 22.971 0.231 0.355 -video archive 0.111 1.440 0.556 0.199 2.306 2.729 0.206 0.320 0.053 19.970 0.173 0.187 -start year 0.167 1.846 0.545 0.307 2.691 7.001 0.385 0.272 0.104 22.228 0.267 0.488 -start match 0.143 1.692 0.571 0.285 2.691 7.001 0.307 0.406 0.078 26.811 0.233 0.443 -game round 0.333 2.539 0.889 0.670 6.964 10.582 0.744 0.278 0.173 28.908 0.662 0.841 -championship tournament 0.333 2.539 0.857 0.667 6.609 5.809 0.633 0.599 0.115 8.792 0.686 0.646 -line insurance 0.200 2.028 0.714 0.442 5.176 10.997 0.559 0.773 0.121 30.854 0.442 0.744 -nature environment 0.167 1.846 0.545 0.307 2.298 5.951 0.336 0.569 0.099 24.681 0.250 0.430 -nature man 0.250 2.251 0.667 0.458 2.283 2.612 0.301 0.201 0.086 27.389 0.357 0.319 -man woman 0.333 2.539 0.706 0.670 4.910 4.037 0.787 0.505 0.273 18.995 0.599 0.552 -man governor 0.200 2.028 0.667 0.449 3.258 3.625 0.402 0.284 0.093 19.424 0.296 0.261 -soap opera 0.071 0.999 0.235 0.040 0.802 2.612 0.075 0.208 0.048 24.390 0.081 0.121 -opera performance 0.100 1.335 0.308 0.089 0.802 2.612 0.084 0.243 0.057 25.844 0.117 0.152 -production crew 0.125 1.558 0.364 0.132 0.596 2.612 0.070 0.208 0.065 21.527 0.140 0.204 -television film 0.200 2.028 0.778 0.449 7.021 9.016 0.720 0.507 0.155 12.746 0.545 0.516 -viewer serial 0.091 1.240 0.500 0.133 2.306 2.729 0.212 0.000 0.061 20.761 0.143 0.155 -possibility girl 0.091 1.240 0.167 0.000 0.000 0.000 0.000 0.000 0.068 25.571 0.091 0.091 -morality marriage 0.167 1.846 0.545 0.307 2.298 5.951 0.306 0.227 0.088 18.793 0.250 0.430 -change attitude 0.167 1.846 0.545 0.307 3.037 5.951 0.350 0.443 0.126 19.479 0.283 0.406 -family planning 0.100 1.335 0.308 0.089 0.596 2.612 0.082 0.241 0.070 23.838 0.113 0.166 -opera industry 0.091 1.240 0.286 0.073 0.596 2.612 0.065 0.249 0.055 22.317 0.103 0.152 -sugar approach 0.091 1.240 0.286 0.073 0.802 2.612 0.087 0.215 0.056 27.373 0.107 0.140 -practice institution 0.333 2.539 0.833 0.659 7.355 13.077 0.871 0.342 0.315 24.597 0.721 0.902 -ministry culture 0.125 1.558 0.533 0.233 3.336 4.982 0.314 0.313 0.064 17.387 0.231 0.235 -problem challenge 0.167 1.846 0.615 0.348 3.848 6.513 0.421 0.499 0.086 22.634 0.287 0.455 -country citizen 0.111 1.440 0.429 0.168 1.290 0.955 0.178 0.161 0.077 18.098 0.143 0.134 -planet people 0.111 1.440 0.200 0.000 0.000 0.000 0.000 0.000 0.072 22.018 0.111 0.111 -development issue 0.250 2.251 0.750 0.458 4.089 9.717 0.567 0.309 0.138 26.311 0.454 0.745 -experience music 0.167 1.846 0.615 0.348 2.645 5.359 0.329 0.251 0.086 16.582 0.240 0.324 -music project 0.250 2.251 0.800 0.546 3.144 5.519 0.432 0.353 0.108 10.556 0.402 0.533 -glass metal 0.167 1.846 0.545 0.307 3.337 5.761 0.454 0.081 0.111 22.755 0.296 0.420 -concert virtuoso 0.077 1.073 0.143 0.000 0.000 0.000 0.000 0.000 0.048 25.137 0.077 0.077 -museum theater 0.143 1.692 0.625 0.296 2.306 2.729 0.248 0.355 0.067 10.918 0.218 0.235 -observation architecture 0.167 1.846 0.706 0.366 3.144 5.519 0.295 0.000 0.067 23.688 0.287 0.407 -space world 0.200 2.028 0.571 0.307 2.298 5.951 0.334 0.192 0.099 28.146 0.222 0.309 -disaster area 0.111 1.440 0.500 0.191 3.140 6.399 0.353 0.259 0.080 25.449 0.201 0.343 -governor office 0.100 1.335 0.526 0.163 2.306 2.729 0.250 0.134 0.067 20.007 0.157 0.170 diff --git a/sematch/eval/word_similarity/type_mc.txt b/sematch/eval/word_similarity/type_mc.txt deleted file mode 100644 index 71a3fa0..0000000 --- a/sematch/eval/word_similarity/type_mc.txt +++ /dev/null @@ -1,18 +0,0 @@ -car automobile 3.92 -gem jewel 3.84 -journey voyage 3.84 -coast shore 3.7 -magician wizard 3.5 -food fruit 3.08 -bird cock 3.05 -tool implement 2.95 -brother monk 2.82 -journey car 1.16 -monk oracle 1.1 -cemetery woodland 0.95 -coast hill 0.87 -forest graveyard 0.84 -shore woodland 0.63 -monk slave 0.55 -coast forest 0.42 -glass magician 0.11 diff --git a/sematch/eval/word_similarity/type_rg.txt b/sematch/eval/word_similarity/type_rg.txt deleted file mode 100644 index 79ad69e..0000000 --- a/sematch/eval/word_similarity/type_rg.txt +++ /dev/null @@ -1,41 +0,0 @@ -gem jewel 3.94 -automobile car 3.92 -cemetery graveyard 3.88 -cushion pillow 3.84 -implement tool 3.66 -forest woodland 3.65 -coast shore 3.6 -journey voyage 3.58 -serf slave 3.46 -hill mound 3.29 -magician wizard 3.21 -brother monk 2.74 -food fruit 2.69 -bird cock 2.63 -oracle sage 2.61 -sage wizard 2.46 -magician oracle 1.82 -glass jewel 1.78 -cemetery mound 1.69 -car journey 1.55 -hill woodland 1.48 -furnace implement 1.37 -coast hill 1.26 -bird woodland 1.24 -shore voyage 1.22 -cemetery woodland 1.18 -forest graveyard 1.0 -mound shore 0.97 -automobile cushion 0.97 -boy sage 0.96 -monk oracle 0.91 -shore woodland 0.9 -coast forest 0.85 -asylum cemetery 0.79 -monk slave 0.57 -cushion jewel 0.45 -glass magician 0.44 -asylum monk 0.39 -asylum fruit 0.19 -automobile wizard 0.11 -fruit furnace 0.05 diff --git a/sematch/eval/word_similarity/type_simlex.txt b/sematch/eval/word_similarity/type_simlex.txt deleted file mode 100644 index d9f6d8e..0000000 --- a/sematch/eval/word_similarity/type_simlex.txt +++ /dev/null @@ -1,408 +0,0 @@ -book text 6.35 -groom bride 3.17 -night day 1.88 -plane airport 3.65 -horse mare 8.33 -bottom top 0.7 -friend buddy 8.78 -student pupil 9.35 -world globe 6.67 -plane jet 8.1 -woman man 3.33 -actress actor 7.12 -teacher instructor 9.25 -movie film 8.87 -bird hawk 7.85 -word dictionary 3.68 -dog cat 1.75 -area region 9.47 -navy army 6.43 -child adult 2.98 -book story 5.63 -taxi cab 9.2 -bed bedroom 3.4 -roof ceiling 7.58 -disease infection 7.15 -boat anchor 2.25 -priest monk 6.28 -river stream 7.3 -date calendar 4.42 -sea ocean 8.27 -wood log 7.3 -mud dirt 7.32 -hallway corridor 9.28 -way manner 7.62 -mouse cat 1.12 -death burial 4.93 -music melody 6.98 -beer alcohol 7.5 -storm hurricane 6.38 -tax income 2.38 -floor ceiling 1.73 -beach seashore 8.33 -hound fox 2.38 -car horn 2.57 -friend guest 4.25 -employer employee 3.65 -ball cannon 2.58 -alcohol brandy 6.98 -victory triumph 8.98 -motel inn 8.17 -clothes cloth 5.47 -band orchestra 7.08 -business industry 7.02 -alcohol gin 8.65 -hat coat 2.67 -window door 3.33 -glass crystal 6.27 -wine brandy 5.15 -creator maker 9.62 -arm muscle 3.72 -death tragedy 5.8 -gun cannon 5.68 -grass blade 4.57 -hose garden 1.67 -boy kid 7.5 -church choir 2.95 -clothes drawer 3.02 -tower bell 1.9 -father parent 7.07 -school grade 4.42 -parent adult 5.37 -bar jail 1.9 -car highway 3.4 -dictionary definition 6.25 -army legion 5.95 -chair bench 6.67 -boy son 6.75 -water ice 6.47 -attorney lawyer 9.35 -area zone 8.33 -business company 9.02 -clothes fabric 5.87 -sweater jacket 7.15 -money capital 6.67 -hand foot 4.17 -alcohol cocktail 6.73 -molecule atom 6.45 -lens camera 4.28 -meal dinner 7.15 -eye tear 3.55 -god devil 1.8 -loop belt 3.1 -rat mouse 7.78 -motor engine 8.65 -car cab 7.42 -cat lion 6.75 -door gate 5.25 -cat pet 5.95 -bone jaw 4.17 -house key 1.9 -door corridor 3.73 -god spirit 7.3 -wall brick 4.68 -horn drum 2.68 -phrase word 5.48 -band parade 3.92 -cloud storm 5.6 -beer beverage 5.42 -intelligence logic 6.5 -communication language 7.47 -mob crowd 7.85 -shore coast 8.83 -water salt 1.3 -sense intuition 7.68 -situation condition 6.58 -crime theft 7.53 -style fashion 8.5 -boundary border 9.08 -arm body 4.05 -boat car 2.37 -bride princess 2.8 -car gauge 1.13 -insect bee 6.07 -animal person 3.05 -bed hospital 0.92 -disc computer 3.2 -grass moss 5.0 -school law 1.13 -foot head 2.3 -mother guardian 6.5 -alcohol whiskey 7.27 -bathroom bedroom 3.4 -plate bowl 5.23 -worker employer 5.37 -son father 3.82 -heart surgery 1.08 -woman secretary 1.98 -man father 4.83 -beach island 5.6 -story topic 5.0 -game fun 3.42 -couple pair 8.33 -woman wife 5.72 -ceiling cathedral 2.42 -wood paper 2.88 -top side 1.9 -crime fraud 5.65 -pain harm 5.38 -lover companion 5.97 -father daughter 2.62 -wine liquor 7.85 -belief opinion 7.7 -pact agreement 9.02 -wealth poverty 1.27 -accident emergency 4.93 -battle conquest 7.22 -friend teacher 2.62 -illness infection 6.9 -game trick 2.32 -brother son 3.48 -worker mechanic 4.92 -bee queen 3.27 -car bicycle 3.47 -army squad 5.08 -cloud weather 4.87 -physician doctor 8.88 -canyon valley 6.75 -river valley 1.67 -opinion choice 5.43 -rhythm melody 6.12 -cattle beef 7.03 -doctor professor 4.65 -arm vein 3.65 -room bath 3.33 -corporation business 9.02 -fun football 1.97 -hill cliff 4.28 -apple candy 2.08 -helper maid 5.58 -leader manager 7.27 -bee ant 2.78 -bed furniture 6.08 -emotion passion 7.72 -crime violation 7.12 -machine engine 5.58 -beach sea 4.68 -jar bottle 7.83 -guitar drum 3.78 -opinion idea 5.7 -diet salad 2.98 -mother wife 3.02 -dad mother 3.55 -captain sailor 5.0 -meter yard 5.6 -motor boat 2.57 -card bridge 1.97 -destruction construction 0.98 -crowd bunch 7.42 -beach reef 3.77 -man child 4.13 -bread cheese 1.95 -champion winner 8.73 -celebration ceremony 7.72 -king princess 3.27 -god priest 4.5 -men fraternity 3.13 -buddy companion 8.65 -teacher helper 4.28 -house carpet 1.38 -intelligence skill 5.35 -journey conquest 4.72 -god prey 1.23 -brother soul 0.97 -adversary opponent 9.05 -death catastrophe 4.13 -monster demon 6.95 -man victor 1.9 -song story 3.97 -box elevator 1.32 -lake swamp 4.92 -flower bulb 4.48 -driver pilot 6.28 -idea image 3.55 -father brother 4.2 -rail fence 5.22 -room bed 2.35 -flower bush 4.25 -bottom side 2.63 -vessel vein 5.15 -cat rabbit 2.37 -meat sandwich 2.35 -belief concept 5.08 -attention interest 7.22 -right justice 7.05 -argument agreement 1.45 -depth magnitude 6.12 -medium news 3.65 -winner candidate 2.78 -birthday date 5.08 -fee payment 7.15 -man sentry 3.25 -whiskey gin 6.28 -roof floor 2.62 -door floor 1.67 -wagon carriage 7.7 -car carriage 5.13 -administration management 7.25 -communication conversation 8.02 -anatomy biology 5.33 -college profession 3.12 -book topic 2.07 -formula equation 7.95 -book information 5.0 -boy partner 1.9 -college class 4.13 -chief mayor 4.85 -rabbi minister 7.62 -lawyer banker 1.88 -violin instrument 6.58 -camp cabin 4.2 -pot appliance 2.53 -girl child 5.38 -adult baby 2.22 -log timber 8.05 -spoon cup 2.02 -body nerve 3.13 -bone neck 2.53 -river sea 5.72 -task job 8.87 -club society 5.23 -prince king 5.92 -people party 2.2 -boy brother 6.67 -root grass 3.55 -money pearl 2.1 -money diamond 3.42 -vehicle bus 6.47 -cab bus 5.6 -house barn 4.33 -finger palm 3.33 -car bridge 0.95 -effort difficulty 4.45 -job management 3.97 -cancer sickness 7.93 -word newspaper 2.47 -composer writer 6.58 -actor singer 4.52 -shelter hut 6.47 -cabin hut 6.53 -value belief 7.07 -idea scheme 6.75 -mood emotion 8.12 -argument criticism 5.08 -aggression hostility 8.48 -vision perception 6.88 -biography fiction 1.38 -discussion argument 5.48 -code symbol 6.03 -danger disease 3.0 -accident catastrophe 5.9 -activity movement 7.15 -gossip news 5.22 -father god 3.57 -action course 5.45 -fever illness 7.65 -aviation flight 8.18 -game action 4.85 -molecule air 3.05 -home state 2.58 -adult guardian 6.9 -newspaper information 5.65 -communication television 5.6 -author reader 1.6 -ballad song 7.53 -wall decoration 2.62 -nurse scientist 2.08 -politician president 7.38 -president mayor 5.68 -book essay 4.72 -man warrior 4.72 -article journal 6.18 -crowd parade 3.93 -teacher rabbi 4.37 -book article 5.43 -room cell 4.58 -daughter kid 4.17 -car elevator 1.03 -clothes coat 5.35 -apple sauce 1.43 -car hose 0.87 -tobacco cigarette 7.5 -student professor 1.95 -baby daughter 5.0 -pipe cigar 6.03 -box cigar 1.25 -cup cone 3.17 -bone teeth 4.17 -cup jar 5.13 -proof fact 7.3 -appointment engagement 6.75 -birthday year 1.67 -word clue 2.53 -author creator 8.02 -atom carbon 3.1 -archbishop bishop 7.05 -muscle bone 3.65 -muscle tongue 5.0 -boy soldier 2.15 -bed chair 3.5 -clothes jacket 5.15 -gun knife 3.65 -bottle container 7.93 -meat bread 1.67 -arm bone 3.83 -assignment task 8.7 -dinner soup 3.72 -dog horse 2.38 -arm neck 1.58 -ball glove 1.75 -nerve brain 3.88 -beast animal 7.83 -dinner chicken 2.85 -girl maid 2.93 -child boy 5.75 -alcohol wine 7.42 -street car 2.38 -bell door 2.2 -box hat 1.3 -belief impression 5.95 -bias opinion 5.6 -anger mood 4.1 -book theme 2.58 -friend mother 2.53 -car factory 2.75 -pact condition 2.45 -belief flower 0.4 -winner goal 3.23 -choice vein 0.98 -hymn conquest 0.68 -jail choice 1.08 -condition boy 0.48 -hole agreement 0.3 -doctor temper 0.48 -fraternity door 0.68 -task woman 0.68 -fraternity baseball 0.88 -mouse management 0.48 -task highway 0.48 -island task 0.3 -gun trick 0.48 -bath trick 0.58 -diet apple 1.18 -dentist colonel 0.4 -hole wife 0.68 -pupil president 0.78 -bath wife 0.48 -formula log 1.77 -woman fur 0.58 -camera president 0.48 -liquor band 0.68 -gun fur 0.3 -worker camera 0.68 -people gun 0.68 -princess island 0.3 -container mouse 0.3 -wagon container 2.65 -bicycle bedroom 0.4 -log bath 0.4 diff --git a/sematch/eval/word_similarity/type_ws353-sim.txt b/sematch/eval/word_similarity/type_ws353-sim.txt deleted file mode 100644 index 3571842..0000000 --- a/sematch/eval/word_similarity/type_ws353-sim.txt +++ /dev/null @@ -1,128 +0,0 @@ -tiger cat 7.35 -tiger tiger 10.0 -plane car 5.77 -train car 6.31 -doctor nurse 7.0 -professor doctor 6.62 -student professor 6.81 -wood forest 7.73 -king queen 8.58 -bishop rabbi 6.69 -vodka gin 8.46 -vodka brandy 8.13 -car automobile 8.94 -gem jewel 8.96 -journey voyage 9.29 -coast shore 9.1 -magician wizard 9.02 -food fruit 7.52 -bird cock 7.1 -money currency 9.04 -tiger jaguar 8.0 -tiger feline 8.0 -tiger carnivore 7.08 -tiger mammal 6.85 -tiger animal 7.0 -tiger organism 4.77 -tiger fauna 5.62 -planet star 8.45 -cup tableware 6.85 -cup artifact 2.92 -cup object 3.69 -jaguar cat 7.42 -jaguar car 7.27 -announcement news 7.56 -doctor personnel 5.0 -hospital infrastructure 4.63 -life death 7.88 -travel activity 5.0 -type kind 8.97 -street place 6.44 -street avenue 8.88 -street block 6.88 -cell phone 7.81 -dividend payment 7.63 -calculation computation 8.44 -phone equipment 7.13 -liquid water 7.89 -seafood food 8.34 -seafood lobster 8.7 -lobster food 7.81 -lobster wine 5.7 -championship tournament 8.36 -man woman 8.3 -man governor 5.25 -opera performance 6.88 -glass metal 5.56 -museum theater 7.19 -monk oracle 5.0 -cup food 5.0 -journal association 4.97 -street children 4.94 -car flight 4.94 -situation conclusion 4.81 -word similarity 4.75 -peace plan 4.75 -consumer energy 4.75 -ministry culture 4.69 -investigation effort 4.59 -image surface 4.56 -life term 4.5 -start match 4.47 -computer news 4.47 -board recommendation 4.47 -observation architecture 4.38 -coast hill 4.38 -deployment departure 4.25 -benchmark index 4.25 -attempt peace 4.25 -start year 4.06 -development issue 3.97 -theater history 3.91 -profit warning 3.88 -peace atmosphere 3.69 -morality marriage 3.69 -minority peace 3.69 -atmosphere landscape 3.69 -report gain 3.63 -music project 3.63 -seven series 3.56 -experience music 3.47 -school center 3.44 -five month 3.38 -announcement production 3.38 -money operation 3.31 -delay news 3.31 -practice institution 3.19 -coast forest 3.15 -shore woodland 3.08 -drink car 3.04 -president medal 3.0 -prejudice recognition 3.0 -viewer serial 2.97 -peace insurance 2.94 -media gain 2.88 -announcement effort 2.75 -line insurance 2.69 -drink mother 2.65 -opera industry 2.63 -volunteer motto 2.56 -cup article 2.4 -sign recess 2.38 -problem airport 2.38 -direction combination 2.25 -glass magician 2.08 -cemetery woodland 2.08 -possibility girl 1.94 -cup substance 1.92 -forest graveyard 1.85 -stock egg 1.81 -month hotel 1.81 -energy secretary 1.81 -stock phone 1.62 -holy sex 1.62 -stock CD 1.31 -stock life 0.92 -stock jaguar 0.92 -monk slave 0.92 -sugar approach 0.88 diff --git a/sematch/eval/word_similarity/type_ws353.txt b/sematch/eval/word_similarity/type_ws353.txt deleted file mode 100644 index b67f143..0000000 --- a/sematch/eval/word_similarity/type_ws353.txt +++ /dev/null @@ -1,211 +0,0 @@ -love sex 6.77 -tiger cat 7.35 -tiger tiger 10.0 -book paper 7.46 -computer keyboard 7.62 -computer internet 7.58 -plane car 5.77 -train car 6.31 -telephone communication 7.5 -drug abuse 6.85 -doctor nurse 7.0 -professor doctor 6.62 -student professor 6.81 -company stock 7.08 -stock market 8.08 -stock phone 1.62 -stock CD 1.31 -stock jaguar 0.92 -stock egg 1.81 -stock life 0.92 -book library 7.46 -bank money 8.12 -wood forest 7.73 -king queen 8.58 -bishop rabbi 6.69 -holy sex 1.62 -law lawyer 8.38 -movie star 7.38 -movie critic 6.73 -movie theater 7.92 -vodka gin 8.46 -vodka brandy 8.13 -drink car 3.04 -baby mother 7.85 -drink mother 2.65 -car automobile 8.94 -gem jewel 8.96 -journey voyage 9.29 -coast shore 9.1 -magician wizard 9.02 -food fruit 7.52 -bird cock 7.1 -tool implement 6.46 -brother monk 6.27 -journey car 5.85 -monk oracle 5.0 -cemetery woodland 2.08 -coast hill 4.38 -forest graveyard 1.85 -shore woodland 3.08 -monk slave 0.92 -coast forest 3.15 -glass magician 2.08 -money currency 9.04 -money wealth 8.27 -money property 7.57 -money possession 7.29 -money bank 8.5 -money deposit 7.73 -money withdrawal 6.88 -money operation 3.31 -tiger jaguar 8.0 -tiger feline 8.0 -tiger carnivore 7.08 -tiger mammal 6.85 -tiger animal 7.0 -tiger organism 4.77 -tiger fauna 5.62 -tiger zoo 5.87 -planet star 8.45 -planet constellation 8.06 -planet galaxy 8.11 -planet space 7.92 -planet astronomer 7.94 -cup coffee 6.58 -cup tableware 6.85 -cup article 2.4 -cup artifact 2.92 -cup object 3.69 -cup drink 7.25 -cup food 5.0 -cup substance 1.92 -cup liquid 5.9 -jaguar cat 7.42 -jaguar car 7.27 -energy secretary 1.81 -energy laboratory 5.09 -computer laboratory 6.78 -weapon secret 6.06 -investigation effort 4.59 -news report 8.16 -canyon landscape 7.53 -image surface 4.56 -discovery space 6.34 -sign recess 2.38 -computer news 4.47 -territory surface 5.34 -atmosphere landscape 3.69 -president medal 3.0 -war troops 8.13 -record number 6.31 -theater history 3.91 -volunteer motto 2.56 -prejudice recognition 3.0 -delay news 3.31 -minister party 6.63 -peace plan 4.75 -minority peace 3.69 -attempt peace 4.25 -government crisis 6.56 -deployment departure 4.25 -deployment withdrawal 5.88 -energy crisis 5.94 -announcement news 7.56 -announcement effort 2.75 -stroke hospital 7.03 -disability death 5.47 -victim emergency 6.47 -journal association 4.97 -doctor personnel 5.0 -school center 3.44 -hospital infrastructure 4.63 -death inmate 5.03 -lawyer evidence 6.69 -life death 7.88 -life term 4.5 -word similarity 4.75 -board recommendation 4.47 -peace atmosphere 3.69 -peace insurance 2.94 -travel activity 5.0 -consumer energy 4.75 -problem airport 2.38 -car flight 4.94 -credit card 8.06 -credit information 5.31 -hotel reservation 8.03 -grocery money 5.94 -month hotel 1.81 -type kind 8.97 -situation conclusion 4.81 -direction combination 2.25 -street place 6.44 -street avenue 8.88 -street block 6.88 -street children 4.94 -listing category 6.38 -cell phone 7.81 -benchmark index 4.25 -media gain 2.88 -dividend payment 7.63 -dividend calculation 6.48 -calculation computation 8.44 -currency market 7.5 -oil stock 6.34 -announcement production 3.38 -announcement warning 6.0 -profit warning 3.88 -computer software 8.5 -phone equipment 7.13 -equipment maker 5.91 -five month 3.38 -report gain 3.63 -liquid water 7.89 -baseball season 5.97 -game victory 7.03 -game team 7.69 -game series 6.19 -seven series 3.56 -seafood sea 7.47 -seafood food 8.34 -seafood lobster 8.7 -lobster food 7.81 -lobster wine 5.7 -food preparation 6.22 -video archive 6.34 -start year 4.06 -start match 4.47 -game round 5.97 -championship tournament 8.36 -line insurance 2.69 -nature environment 8.31 -nature man 6.25 -man woman 8.3 -man governor 5.25 -soap opera 7.94 -opera performance 6.88 -production crew 6.25 -television film 7.72 -viewer serial 2.97 -possibility girl 1.94 -morality marriage 3.69 -change attitude 5.44 -family planning 6.25 -opera industry 2.63 -sugar approach 0.88 -practice institution 3.19 -ministry culture 4.69 -problem challenge 6.75 -country citizen 7.31 -planet people 5.75 -development issue 3.97 -experience music 3.47 -music project 3.63 -glass metal 5.56 -concert virtuoso 6.81 -museum theater 7.19 -observation architecture 4.38 -space world 6.53 -disaster area 6.25 -governor office 6.34 diff --git a/sematch/evaluation.py b/sematch/evaluation.py index 873b109..3cf5eea 100644 --- a/sematch/evaluation.py +++ b/sematch/evaluation.py @@ -1,3 +1,20 @@ +#!/usr/bin/python +# -*- coding: utf-8 -*- +# Copyright 2017 Ganggao Zhu- Grupo de Sistemas Inteligentes +# gzhu[at]dit.upm.es +# DIT, UPM +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. from __future__ import division @@ -156,7 +173,7 @@ def load_dataset(self, dataset_name): :param dataset_name: the file name of word similarity dataset :return: word pairs and huamn ratings """ - data = FileIO.read_list_file('eval/word_similarity/%s.txt' % dataset_name) + data = FileIO.read_list_file('dataset/wordsim/%s.txt' % dataset_name) #print "dataset ", dataset_name, " ", len(data), " word pairs" word_pairs = map(lambda x: (x.split()[0], x.split()[1]), data) human = map(float, map(lambda x: x.split()[2], data)) @@ -170,7 +187,7 @@ def load_result(self, sim_name, dataset_name): :param dataset_name: the name of word similarity dataset :return: cor relation score and rating scores generated by similarity metric """ - data = FileIO.read_list_file('eval/word_similarity/results/%s-%s.txt' % (dataset_name, sim_name)) + data = FileIO.read_list_file('dataset/wordsim/results/%s-%s.txt' % (dataset_name, sim_name)) data = map(float, data) return data[0], data[1:] @@ -185,7 +202,7 @@ def save_result(self, cor, sim_values, sim_name, dataset_name): """ data = ["%.3f" % cor] data += map(lambda x: "%.3f" % x, sim_values) - FileIO.save_list_file('eval/word_similarity/results/%s-%s.txt' % (dataset_name, sim_name), data) + FileIO.save_list_file('dataset/wordsim/results/%s-%s.txt' % (dataset_name, sim_name), data) def check_word_graph(self, w1, w2): @@ -203,7 +220,6 @@ def check_word_graph(self, w1, w2): return True if graph_ic else False - def check_word_type(self, w1, w2): """ check if both words are used as type in DBpedia @@ -254,7 +270,7 @@ def separate_dataset(self, in_file, out_file, check_function): h = human[i] if check_function(w1, w2): out_data.append(' '.join([w1, w2, str(h)])) - FileIO.save_list_file('eval/word_similarity/%s.txt' % out_file, out_data) + FileIO.save_list_file('dataset/wordsim/%s.txt' % out_file, out_data) # data = FileIO.read_list_file('eval/lexical/SimLex-999.txt') # data = data[1:] @@ -274,7 +290,11 @@ def __init__(self, metric='Spearman'): self._steiger_test = SteigerTest() self._correlation = lambda x, y: spearmanr(x, y) if metric == 'Spearman' else lambda x, y: pearsonr(x, y) - def evaluate_single_metric(self, sim_name, sim_func, dataset_name, save_results=False): + def dataset_names(self): + return ['noun_mc', 'noun_rg', 'noun_simlex', + 'noun_ws353', 'noun_ws353-sim', 'rg65_spanish','rg65_EN-ES'] + + def evaluate_metric(self, sim_name, sim_func, dataset_name, save_results=False): """ Evaluate a single similarity metric for a dataset @@ -303,7 +323,7 @@ def evaluate_multiple_metrics(self, sim_dict, dataset_name): """ cor_dict = {} for sim_name, sim_func in sim_dict.iteritems(): - cor_dict[sim_name] = self.evaluate_single_metric(sim_name, sim_func, dataset_name, save_results=True) + cor_dict[sim_name] = self.evaluate_metric(sim_name, sim_func, dataset_name, save_results=True) return cor_dict def evaluate_wpath_k(self, dataset_name, ic='corpus'): @@ -319,7 +339,7 @@ def evaluate_wpath_k(self, dataset_name, ic='corpus'): sim_func = lambda x, y: self._dataset._yago.word_similarity_wpath(x, y, k/10.0) else: sim_func = lambda x, y: self._dataset._yago.word_similarity_wpath_graph(x, y, k / 10.0) - cor_dic[k] = self.evaluate_single_metric('wpath-'+ str(k), sim_func, dataset_name) + cor_dic[k] = self.evaluate_metric('wpath-'+ str(k), sim_func, dataset_name) return cor_dic @@ -342,65 +362,22 @@ def statistical_test(self, sim_name, compare_name, dataset_name): return cor_z, s -class TextSimEvaluation: - - """ - This class is used for evaluating text similarity metrics - """ - - def __init__(self, sim_metric): - self._sim_metric = sim_metric - - def load_dataset(self, dataset_file): - """ - Generate sentence pairs. - :param dataset_file: dataset file - :return: sentence pairs - """ - data = FileIO.read_list_file(dataset_file) - data = [d.strip() for d in data] - corpus = [] - for d in data: - item = d.split('\t') - corpus.append((item[0], item[1])) - return corpus - - def evaluate(self, input_file, output_file): - """ - Evaluate the sentence similarity - :param input_file: corpus file - :param output_file: result file - :return: similarity scores of text pairs - """ - corpus = self.load_dataset(input_file) - print 'dataset size: ', len(corpus) - result = [self._sim_metric(t1, t2) for t1, t2 in corpus] - result = map(lambda x:"%.3f" % round(x,3), result) - FileIO.save_list_file(output_file, result) - return result - -class ABSAEvaluation: +class AspectEvaluation: """ - This class is used for aspect based sentiment analysis evaluation + This class is used for aspect category classification. """ def __init__(self): pass - def load_dataset(self, dataset_file, cat_full=False): - from BeautifulSoup import BeautifulSOAP as bs - pairs = [] - with open(FileIO.filename(dataset_file), 'r') as f: - corpus = f.read() - opinions = bs(corpus).findAll('opinion') - for op in opinions: - if not op['target'] == 'NULL': - t = op['target'] - c = op['category'] if cat_full else op['category'].split('#')[0] - pairs.append((t, c)) - X, y = zip(*pairs) + def load_dataset(self): + data = FileIO.read_json_file('dataset/aspect/data.txt') + X, y = zip(*[(d['text'], d['label']) for d in data]) return X, y def evaluate(self, X, y, classifier, detailed_report=True): + print "Evaluating..." pred = classifier.classify(X) generate_report(list(y), list(pred), list(set(y)), detailed_report) + + diff --git a/sematch/linking.py b/sematch/linking.py deleted file mode 100644 index 83cd4f3..0000000 --- a/sematch/linking.py +++ /dev/null @@ -1,253 +0,0 @@ - -from sematch.sparql import NameSPARQL -import requests - - -class DBpediaSpotlight: - """simple wrapper implementation of dbpedia spotlight web service using local server""" - - - def __init__(self): - self.uri = 'http://localhost:8888/rest/' - self.dbr = 'http://dbpedia.org/resource/' - self.confidence = 0.5 - self.support = 20 - - def create_obj(self, x): - obj = {} - obj['uri'] = self.dbr + x['@uri'] - obj['label'] = x['@label'] - obj['score'] = x['@finalScore'] - obj['context'] = x['@contextualScore'] - obj['support'] = x['@support'] - return obj - - def take_resource(self, data): - resource_list = [] - if type(data['resource']) is list: - for x in data['resource']: - resource_list.append(self.create_obj(x)) - elif type(data['resource']) is dict: - x = data['resource'] - resource_list.append(self.create_obj(x)) - return resource_list - - def mentions(self, res): - data = {} - data['mention'] = res['@name'] - data['offset'] = res['@offset'] - data['mappings'] = self.take_resource(res) - return data - - def service(self, url, text): - headers = {'Accept': 'application/json'} - params={ - "text": text, - "confidence": self.confidence, - "support": self.support, - } - return requests.get(url,params=params,headers=headers) - - - #use dbpedia spotlight to generate candidate dbpedia resources - def candidates(self, text): - url = self.uri+'candidates' - result = self.service(url, text).json() - results = [] - if result['annotation'].get('surfaceForm'): - result = result['annotation']['surfaceForm'] - if type(result) is list: - for res in result: - results.append(self.mentions(res)) - elif type(result) is dict: - results.append(self.mentions(result)) - return results - - #use dbpedia spotlight to annotate text - def annotate(self, text): - url = self.uri + 'annotate' - result = self.service(url, text).json() - results = {} - if result.get('Resources'): - for res in result['Resources']: - results[res['@surfaceForm']] = res['@URI'] - return results - -class TAGME: - # a python wrapper of tagme web api - # http://tagme.di.unipi.it/ - - - def __init__(self, key): - self.uri = 'http://tagme.di.unipi.it/tag' - self.dbr = 'http://dbpedia.org/resource/' - self.rho = 0.1 - self.key = key - - def service(self, url, text): - headers = {'Accept': 'application/json'} - params={ - "text": text, - "lang": 'en', - "key": self.key - } - return requests.get(url,params=params,headers=headers) - - #use dbpedia spotlight to annotate text - def annotate(self, text): - result = {} - data = self.service(self.uri, text).json() - if data.get('annotations'): - for d in data['annotations']: - if float(d['rho']) > self.rho: - result[d['spot']] = self.dbr + '_'.join(d['title'].split()) - return result - -import urllib2 -import urllib -import json -import gzip - -from StringIO import StringIO - - -class BabelNet: - - def __init__(self, key, langs='EN'): - self.service_disambiguate = 'https://babelfy.io/v1/disambiguate' - self.service_synsetIds = 'https://babelnet.io/v2/getSynsetIds' - self.service_synset = 'https://babelnet.io/v2/getSynset' - self.api_key = key - self.langs = langs - - def service(self, url, params): - params['key'] = self.api_key - params['langs'] = self.langs - url = url + '?' + urllib.urlencode(params) - request = urllib2.Request(url) - request.add_header('Accept-encoding', 'gzip') - response = urllib2.urlopen(request) - data = [] - if response.info().get('Content-Encoding') == 'gzip': - buf = StringIO( response.read()) - f = gzip.GzipFile(fileobj=buf) - data = json.loads(f.read()) - return data - - def synset_id(self, word): - params= { - 'word': word, - 'pos':'NOUN', - } - data = self.service(self.service_synsetIds,params) - return data - - def synset(self, id): - params = { - 'id':id, - 'filterLangs':'EN' - } - data = self.service(self.service_synset, params) - return data - - def wn_synsets(self, word): - data = self.synset_id(word) - data = [self.synset(d['id']) for d in data] - data = [self.wn_offset(d) for d in data] - return [d for d in data if d is not None] - - def wn_offset(self, synset): - if synset.get('wnOffsets'): - return synset['wnOffsets'] - return None - - def disambiguate(self, text): - params={ - 'text': text, - } - data = self.service(self.service_disambiguate, params) - print data - # retrieving data - for result in data:# retrieving token fragment - tokenFragment = result.get('tokenFragment') - tfStart = tokenFragment.get('start') - tfEnd = tokenFragment.get('end') - print str(tfStart) + "\t" + str(tfEnd) - - # retrieving char fragment - charFragment = result.get('charFragment') - cfStart = charFragment.get('start') - cfEnd = charFragment.get('end') - print str(cfStart) + "\t" + str(cfEnd) - - # retrieving BabelSynset ID - synsetId = result.get('babelSynsetID') - print synsetId - - def annotate(self, text): - params={ - 'text': text, - 'annType':'NAMED_ENTITIES' - } - data = self.service(self.service_disambiguate, params) - result = [] - for d in data: - if d.get('DBpediaURL'): - result.append(d['DBpediaURL']) - return list(set(result)) - - -# -# class Matching: -# -# def __init__(self, tokens): -# self.tokens = tokens -# self.N = len(self.tokens) -# self.segments = [(j,j+i) for i in range(1, self.N + 1) for j in range(self.N - i + 1)] -# self.segment2entity = {} -# -# def entities(self): -# for s in self.segments: -# sf = self.surface_form(s).lower() -# entities = entity_candidates(sf) -# if entities: -# self.add(s, entities) -# span = self.maximun_span() -# result = {} -# for seg in self.segment2entity: -# x,y = seg -# if y - x == span: -# entities = self.segment2entity[seg] -# sf = self.surface_form(seg) -# for entity in entities: -# result[entity] = self.uri_similarity(sf,entity) -# return result -# -# def uri_similarity(self, text, entity): -# fragment = entity[28:] -# #fragment = re.sub(r'\([^)]*\)', '', fragment) -# x = str(text).lower() -# y = str(fragment).lower() -# return string_similarity(x,y) -# -# def surface_form(self, segment): -# begin, end = segment -# return ' '.join(self.tokens[begin:end]) -# -# #add the mapped entities into lexicon -# def add(self, segment, entities): -# map(lambda x:self.add_entry(segment, x), entities) -# -# def add_entry(self, segment, entity): -# self.segment2entity.setdefault(segment, []).append(entity) -# -# def maximun_span(self): -# max_span = 1 -# for seg in self.segment2entity: -# x,y = seg -# span = y - x -# if span > max_span: -# max_span = span -# return max_span - - diff --git a/sematch/models/dbpedia_type_ic.txt b/sematch/models/dbpedia_type_ic.txt index 7de790c..cf2671b 100644 --- a/sematch/models/dbpedia_type_ic.txt +++ b/sematch/models/dbpedia_type_ic.txt @@ -1,2 +1,8 @@ {"ic": "2.64673392481", "concept": "http://dbpedia.org/ontology/Species"} {"ic": "0.0", "concept": "http://dbpedia.org/ontology/Organ"} +{"ic": "0.0", "concept": "http://dbpedia.org/ontology/Instrument"} +{"ic": "0.0", "concept": "http://dbpedia.org/ontology/Guitar"} +{"ic": "4.78763901659", "concept": "http://dbpedia.org/ontology/Device"} +{"ic": "6.46464483752", "concept": "http://dbpedia.org/ontology/Actor"} +{"ic": "3.69680036114", "concept": "http://dbpedia.org/ontology/Film"} +{"ic": "5.32659164202", "concept": "http://dbpedia.org/ontology/City"} diff --git a/sematch/models/yago_type_ic.txt b/sematch/models/yago_type_ic.txt index 671d6e5..d8dd880 100644 --- a/sematch/models/yago_type_ic.txt +++ b/sematch/models/yago_type_ic.txt @@ -2887,3 +2887,4 @@ {"ic": "10.6198007455", "concept": "http://dbpedia.org/class/yago/Colonel109937250"} {"ic": "0.0", "concept": "http://dbpedia.org/class/yago/Fur101899062"} {"ic": "0.0", "concept": "http://dbpedia.org/class/yago/Fur103404149"} +{"ic": "6.78147553993", "concept": "http://dbpedia.org/class/yago/Dancer109989502"} diff --git a/sematch/nlp.py b/sematch/nlp.py index 9176d8d..b51b7b6 100644 --- a/sematch/nlp.py +++ b/sematch/nlp.py @@ -1,113 +1,115 @@ -from nltk.stem import WordNetLemmatizer, PorterStemmer, LancasterStemmer -from nltk.corpus import stopwords -from nltk.tokenize import RegexpTokenizer -from nltk import RegexpParser -import nltk +#!/usr/bin/python +# -*- coding: utf-8 -*- +# Copyright 2017 Ganggao Zhu- Grupo de Sistemas Inteligentes +# gzhu[at]dit.upm.es +# DIT, UPM +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. from sematch.utility import FileIO + +from nltk.stem import WordNetLemmatizer +import nltk + from collections import Counter import itertools import string import re -StopWords = stopwords.words('english') -FunctionWords = ['about', 'across', 'against', 'along', 'around', 'at', - 'behind', 'beside', 'besides', 'by', 'despite', 'down', - 'during', 'for', 'from', 'in', 'inside', 'into', 'near', 'of', - 'off', 'on', 'onto', 'over', 'through', 'to', 'toward', - 'with', 'within', 'without', 'anything', 'everything', - 'anyone', 'everyone', 'ones', 'such', 'it', 'itself', - 'something', 'nothing', 'someone', 'the', 'some', 'this', - 'that', 'every', 'all', 'both', 'one', 'first', 'other', - 'next', 'many', 'much', 'more', 'most', 'several', 'no', 'a', - 'an', 'any', 'each', 'no', 'half', 'twice', 'two', 'second', - 'another', 'last', 'few', 'little', 'less', 'least', 'own', - 'and', 'but', 'after', 'when', 'as', 'because', 'if', 'what', - 'where', 'which', 'how', 'than', 'or', 'so', 'before', 'since', - 'while', 'although', 'though', 'who', 'whose', 'can', 'may', - 'will', 'shall', 'could', 'be', 'do', 'have', 'might', 'would', - 'should', 'must', 'here', 'there', 'now', 'then', 'always', - 'never', 'sometimes', 'usually', 'often', 'therefore', - 'however', 'besides', 'moreover', 'though', 'otherwise', - 'else', 'instead', 'anyway', 'incidentally', 'meanwhile'] -SpecialWords = ['.', ',', '?', '"', '``', "''", "'", '--', '-', ':', ';', '(', - ')', '$', '000', '1', '2', '10,' 'I', 'i', 'a', ] -NOUNS = ['nn', 'nns', 'nn$', 'nn-tl', 'nn+bez', 'nn+hvz', 'nns$', 'np', - 'np$', 'np+bez', 'nps', 'nps$', 'nr', 'np-tl', 'nrs', 'nr$'] - -StopWords = set(StopWords) -FunctionWords = set(FunctionWords) - -SpecialWords = set(SpecialWords) -NOUNS = set(NOUNS) - -lemma = WordNetLemmatizer() -porter = PorterStemmer() -lancaster = LancasterStemmer() - - -#r'(?u)\b\w\w+\b' -#r'[a-z]+' -#r'\w+' -#r'''(?x) -#([A-Z]\.)+ # abbreviations, e.g. U.S.A. -#| \w+(-\w+)* # words with optional internal hyphens -#| \$?\d+(\.\d+)?%? # currency and percentages, e.g. $12.40, 82% -#| \w+[\x90-\xff] # these are escaped emojis -#| [][.,;"'?():-_`] # these are separate tokens -#''' - -tokenization_pattern = r'\w+' - -reg_tokenizer = RegexpTokenizer(tokenization_pattern) -def reg_tokenize(text): - return reg_tokenizer.tokenize(text) - -def remove_stopwords(tokens): - return [w for w in tokens if w.lower() not in StopWords] - -def clean_context(text): - tokens = reg_tokenize(text) - tokens = remove_stopwords(tokens) - return ' '.join(tokens) - -def is_noun(tag): - return tag.lower() in NOUNS +stopwords = set(nltk.corpus.stopwords.words('english')) +wn_lemma = WordNetLemmatizer() +reg_tokenizer = nltk.RegexpTokenizer(r'\w+') +word_pattern = re.compile('[a-zA-Z]+') def word_tokenize(text): - tokens = reg_tokenize(text.lower()) - tokens = [w for w in tokens if w not in StopWords] - tokens = [w for w in tokens if w not in FunctionWords] - return tokens + tokens = reg_tokenizer.tokenize(text) + return list(filter(lambda token: word_pattern.match(token) and len(token) >= 1, tokens)) def sent_tokenize(text): - return re.compile(u'[.!?,;:\t\\\\"\\(\\)\\\'\u2019\u2013]|\\s\\-\\s').split(text) - -def lemmatization(tokens): - tokens = [lemma.lemmatize(w) for w in tokens] - #tokens = [porter.stem(w) for w in tokens] - return tokens - -def tokenize(text): - min_length = 3 - words = map(lambda word: word.lower(), word_tokenize(text)) - words = [word for word in words if word not in StopWords] - tokens = (list(map(lambda token: porter.stem(token), words))) - p = re.compile('[a-zA-Z]+') - return list(filter(lambda token: p.match(token) and len(token) >= min_length, tokens)) - -def tf_idf(docs): - from sklearn.feature_extraction.text import TfidfVectorizer - return TfidfVectorizer(tokenizer=tokenize, min_df=3, - max_df=0.90, max_features=3000, - use_idf=True, sublinear_tf=True, - norm='l2').fit(docs) - -def feature_values(doc, vectorizer): - doc_representation = vectorizer.transform([doc]) - features = vectorizer.get_feature_names() - return [(features[index], doc_representation[0, index]) - for index in doc_representation.nonzero()[1]] + return re.compile(u'[.!?,;:\t\\\\"\\(\\)\\\'\u2019\u2013]|\\s\\-\\s').split(text) + +def word_process(words): + words = [w.lower() for w in words] + words = [w for w in words if w not in stopwords] + words = [wn_lemma.lemmatize(w) for w in words] + words = [wn_lemma.lemmatize(w, pos='v') for w in words] + return words + +chunk_grammar_phrases = """CHUNK: {} + {} + {} + {+}""" + +class Extraction: + + """This class is used to extract nouns, proper nouns, phrases from text, including descriptions and + url links""" + + def __init__(self, w_tokenize=None, s_tokenize=None, + pos_tag=None, stop_words=None, punct=None, + grammar=chunk_grammar_phrases): + self._word_tokenize = w_tokenize if w_tokenize else word_tokenize + self._sent_tokenize = s_tokenize if s_tokenize else sent_tokenize + self._tagger = pos_tag if pos_tag else nltk.pos_tag + self._stop_words = stop_words if stop_words else stopwords + self._punct = punct if punct else set(string.punctuation) + self._chunk_grammar = grammar + self._chunker = nltk.RegexpParser(self._chunk_grammar) + + def extract_chunks_sent(self, sent): + """ + Extract chunk phrases from a sentence. + :param sent: a sentence level text. + :return: chunk phrases + """ + tags = self._tagger(self._word_tokenize(sent)) + chunks = nltk.chunk.tree2conlltags(self._chunker.parse(tags)) + # join constituent chunk words into a single chunked phrase + return [' '.join(word for word, pos, chunk in group) + for key, group in itertools.groupby(chunks, lambda (word, pos, chunk): chunk != 'O') if key] + + def extract_chunks_doc(self, text): + """ + Extract chunk phrases from a document. + :param text: a document level text + :return: chunk phrases + """ + sents = self._sent_tokenize(text) + sents = [s for s in sents if s] + return list(itertools.chain.from_iterable(map(self.extract_chunks_sent, sents))) + + def extract_words(self, text, good_tags=set(['NN','NNS','NNP','VBP', 'VBZ','VBG','VBN'])): + pos_tags = self._tagger(self._word_tokenize(text)) + words = [word for word, tag in pos_tags if tag in good_tags + and not all(char in self._punct for char in word)] + return word_process(words) + + def extract_nouns(self, text): + return self.extract_words(text, set(['NN', 'NNS', 'NNP'])) + + def extract_verbs(self, text): + return self.extract_words(text, set(['VBP', 'VBZ','VBG','VBN'])) + + def category_features(self, categories): + return map(lambda x:x.replace('http://dbpedia.org/resource/Category:', ''), categories) + + def category2words(self, categories): + words = [] + for cat in categories: + cat_str = ' '.join(cat.split('_')) + words.extend(self.extract_nouns(cat_str)) + return list(set(words)) + class RAKE: @@ -116,9 +118,10 @@ class RAKE: https://github.com/aneesha/RAKE/blob/master/rake.py To make a class for rake python implementation for easy incorporation in other modules. + This class is used to extract keywords from textual document, such as description of entity from DBpedia. """ - def __init__(self, stopwords_file, word_tokenize=None, sent_tokenize=None): + def __init__(self, stopwords_file='models/FoxStoplist.txt', word_tokenize=None, sent_tokenize=None): self._stopwords_pattern = self.build_patterns(self.load_stopwords(stopwords_file)) self._sent_tokenize = sent_tokenize if sent_tokenize else nltk.sent_tokenize self._word_tokenize = word_tokenize if word_tokenize else nltk.word_tokenize @@ -172,82 +175,4 @@ def extract(self, text, ratio=3): phrases, _ = zip(*phrases) return phrases -chunk_grammar_nounphrase=r'KT: {(* + )? * +}' -#chunk_grammar=r'T: {<(JJ|NN|NNS|NNP|NNPS)>+<(NN|NNS|NNP|NNPS|CD)>|<(NN|NNS|NNP|NNPS)>}' -chunk_grammar_phrases = """CHUNK: {
} - {} - {} - {} - {} - {} - {+} - {+}""" -chunk_grammar_propernouns = """CHUNK: {} - {} - {} - {+}""" - - - -class Extraction: - - """This class is used to extract nouns, proper nouns, phrases from text""" - - def __init__(self, word_tokenize=None, sent_tokenize=None, - pos_tag=None, stop_words=None, punct=None, - grammar=chunk_grammar_propernouns): - self._word_tokenize = word_tokenize if word_tokenize else nltk.word_tokenize - self._sent_tokenize = sent_tokenize if sent_tokenize else nltk.sent_tokenize - self._pos_tag = pos_tag if pos_tag else nltk.pos_tag - self._stop_words = stop_words if stop_words else set(nltk.corpus.stopwords.words('english')) - self._punct = punct if punct else set(string.punctuation) - self._chunk_grammar = grammar - self._chunker = RegexpParser(self._chunk_grammar) - - def extract_chunks_sent(self, sent): - """ - Extract chunk phrases from a sentence. - :param sent: a sentence level text. - :return: chunk phrases - """ - tags = self._pos_tag(self._word_tokenize(sent)) - chunks = nltk.chunk.tree2conlltags(self._chunker.parse(tags)) - # join constituent chunk words into a single chunked phrase - return [' '.join(word for word, pos, chunk in group) - for key, group in itertools.groupby(chunks, lambda (word, pos, chunk): chunk != 'O') if key] - - def extract_chunks_doc(self, text): - """ - Extract chunk phrases from a document. - :param text: a document level text - :return: chunk phrases - """ - sents = self._sent_tokenize(text) - sents = [s for s in sents if s] - return list(itertools.chain.from_iterable(map(self.extract_chunks_sent, sents))) - - def extract_words_sent(self, sent, good_tags=set(['NN', 'NNS'])): - """ - Extract desired words from a sentence. - :param sent: a sentence level text - :param good_tags: desired word tags - :return: words with desired word tags - """ - tagged_words = self._pos_tag(self._word_tokenize(sent)) - words = [word for word, tag in tagged_words - if tag in good_tags and word.lower() not in self._stop_words - and not all(char in self._punct for char in word)] - return list(set(words)) - def extract_words_doc(self, text, good_tags=set(['NN', 'NNS'])): - """ - Extract desiredwords from document - :param text: a document level text - :param good_tags: desired word tags - :return: words with desired word tags - """ - sents = self._sent_tokenize(text) - sents = [s for s in sents if s] - func_extract = lambda x: self.extract_words_sent(x, good_tags) - words = list(itertools.chain.from_iterable(map(func_extract, sents))) - return list(set(words)) \ No newline at end of file diff --git a/sematch/ontology.py b/sematch/ontology.py deleted file mode 100644 index 5433f38..0000000 --- a/sematch/ontology.py +++ /dev/null @@ -1,112 +0,0 @@ -from rdflib import RDF, RDFS, OWL -from sematch.utility import FileIO -import rdflib - -class DBpedia: - - def __init__(self, src='models/dbpedia_2015-04.owl'): - self.graph = rdflib.Graph() - self.graph.parse(FileIO.filename(src)) - self.root = 'http://www.w3.org/2002/07/owl#Thing' - self.classes = [s for s in self.graph.subjects(RDF.type, OWL.Class)] - self.o_properties = [s for s in self.graph.subjects(RDF.type, OWL.ObjectProperty)] - self.d_properties = [s for s in self.graph.subjects(RDF.type, OWL.DatatypeProperty)] - self.uri2class = {c.toPython():c for c in self.classes} - self.uri2class[self.root] = rdflib.URIRef(self.root) - self.class_labels = [self.token(c) for c in self.classes] - - def superClass(self, x): - return [o.toPython() for s, v, o in self.graph.triples((self.uri2class[x], RDFS.subClassOf, None))] - - def subClass(self, x): - return [s.toPython() for s, v, o in self.graph.triples((None, RDFS.subClassOf, self.uri2class[x]))] - - def allSubClass(self, x, subList = []): - for sub in self.subClass(x): - subList.append(sub) - self.allSubClass(sub, subList) - return subList - - def allSuperClass(self, x, superList = []): - for super in self.superClass(): - superList.append(super) - self.allSuperClass(super, superList) - return superList - - def siblingsClass(self, x): - siblings = [] - for parent in self.superClass(x): - for child in self.subClass(parent): - if child != x: - siblings.append(child) - return siblings - - def token(self, x): - t = [o for o in self.graph.objects(x, RDFS.label) if o.language=='en'] - if t == []: - return None - return t[0].toPython() - - def lexicon(self, tag, lst): - return [(self.token(x), x.n3(), tag) for x in lst if self.token(x)] - - def range(self, p): - return [o for o in self.graph.objects(p, RDFS.range)] - - def domain(self, p): - return [o for o in self.graph.objects(p, RDFS.domain)] - - - -# class Matcher: -# -# def __init__(self): -# self.sim = Similarity() -# self.string_sim_th = 0.9 -# self.ngram = 5 -# -# def token_filter(self, tokens): -# tags = self.token_pos_split(tokens,1) -# if len(tokens) == 1: -# if tags[0] not in ['NN','NNS','NNP','NNPS','VB','VBD','VBG','VBN','VBP','VBZ']: -# return False -# for t in tags: -# if t in ['WRB','WDT', 'WP', 'WP$','CD']: -# return False -# return True -# -# #i=0, tokens; i=1 pos -# def token_pos_split(self, tokens, i=0): -# return [token[i] for token in tokens] -# -# def lexicons(self, query): -# tokens_lst = self.segmentation(query) -# tokens_lst = filter(self.token_filter, tokens_lst) -# matchers = [self.instance_matching, self.class_matching, self.property_matching] -# return reduce(lambda x,y:x+y, [self.matching(tokens_lst, m) for m in matchers]) -# -# def matching(self, tokens_lst, matcher): -# split = self.token_pos_split -# matches = [matcher(split(tokens)) for tokens in tokens_lst if matcher(split(tokens))] -# if matches: -# matches = reduce(lambda x,y:x+y, matches) -# return matches -# -# def sim_string(self, tokens, lex): -# x = ''.join(tokens).lower().decode('utf-8') -# y = lex[0].replace(' ','').lower() -# return self.sim.string(x,y) > self.string_sim_th -# -# def lex_form(self, tokens, lex): -# return (' '.join(tokens), lex[1], lex[2]) -# -# def token_matching(self, tokens, lex, sim, lexicon): -# return [lex(tokens, l) for l in lexicon if sim(tokens, l)] -# -# def class_matching(self, tokens): -# classes = self.ontology.T_Lexicon() -# return self.token_matching(tokens, self.lex_form, self.sim_string, classes) -# -# def property_matching(self, tokens): -# properties = self.ontology.P_Lexicon() -# return self.token_matching(tokens, self.lex_form, self.sim_string, properties) diff --git a/sematch/search.py b/sematch/search.py deleted file mode 100644 index c50187a..0000000 --- a/sematch/search.py +++ /dev/null @@ -1,38 +0,0 @@ -from sematch.semantic.similarity import YagoTypeSimilarity -from sematch.nlp import Extraction -from sematch.sparql import NameSPARQL, QueryGraph - -import itertools - -class EntitySearch: - - """This class is used for concept based entity search in DBpedia""" - - def __init__(self): - self._linker = NameSPARQL() - self._extracter = Extraction() - self._yago = YagoTypeSimilarity() - self._query_graph = QueryGraph() - - def query_process(self, query): - """ - Process query into concept (common noun) and entity (proper noun). Link them - to Knowledge Graph uri links respectively. - :param query: short text query - :return: tuple of concepts and entities in uris. - """ - concepts = self._extracter.extract_words_sent(query) - entities = self._extracter.extract_chunks_sent(query) - concept_uris = list(itertools.chain.from_iterable(map(self._yago.word2yago, concepts))) - entity_uris = list(itertools.chain.from_iterable(map(self._linker.name2entities, entities))) - return list(set(concept_uris)), list(set(entity_uris)) - - def search(self, query): - results = [] - concepts, entities = self.query_process(query) - for e in entities: - for i in xrange(0, len(concepts), 5): - results.extend(self._query_graph.type_entity_query(concepts[i:i + 5], e)) - return list(set(results)) - - diff --git a/sematch/semantic/graph.py b/sematch/semantic/graph.py index 7b1cd94..7c6ed21 100644 --- a/sematch/semantic/graph.py +++ b/sematch/semantic/graph.py @@ -1,10 +1,31 @@ +#!/usr/bin/python +# -*- coding: utf-8 -*- +# Copyright 2017 Ganggao Zhu- Grupo de Sistemas Inteligentes +# gzhu[at]dit.upm.es +# DIT, UPM +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from abc import ABCMeta, abstractmethod from collections import deque -import networkx as nx from itertools import combinations + +import networkx as nx import numpy as np -from sematch.ontology import DBpedia +from sematch.semantic.ontology import DBpedia +from sematch.utility import FileIO + class DataTransform: """ @@ -121,10 +142,10 @@ def least_common_subsumer(self, node1, node2): return lcs def hyponyms(self, node): - return self._hyponyms[node] if self._hyponyms.get(node) else [] + return self._hyponyms[node] if node in self._hyponyms else [] def hypernyms(self, node): - return self._hypernyms[node] if self._hypernyms.get(node) else [] + return self._hypernyms[node] if node in self._hypernyms else [] class SimGraph: ''' @@ -142,8 +163,6 @@ def __init__(self, nodes, sim_metric, threshold=0.0): self._nodes = nodes self._sim_metric = sim_metric self._threshold = threshold - self._node2id = {n: i for i, n in enumerate(self._nodes)} - #self.id2object = {i:self.object_list[i] for i in range(len(self.object_list))} self._graph = self.similarity_graph(self.similarity_matrix()) def similarity_matrix(self): @@ -176,16 +195,57 @@ def hits(self): def minimum_spanning_tree(self): return nx.minimum_spanning_tree(self._graph) +from sematch.semantic.sparql import StatSPARQL +import math +class GraphIC: -# -# entity_list = ['Steve Jobs', 'Steve Wozniak', 'Jonathan Ivex', 'Mac Pro'] -# -# sim = lambda x, y: len(x) + len(y) -# -# sim_graph = SimGraph(entity_list, sim, 0) -# print sim_graph.page_rank() -# print sim_graph.hits() -# sim_graph.draw2file(sim_graph.minimum_spanning_tree()) + """ + This class is used to compute graph-based IC in knowledge graph, which is + basically the proportion of instances tagged with a specific concept + """ + + def __init__(self, ic_file): + self._ic_file = ic_file + self._graph_ic = self.graph_ic_reader(ic_file) + self._graph_stats = StatSPARQL() + self._N = self._graph_stats.entity_N() + + def concept_ic(self, concept): + """ + Compute the ic value of a concept using sparql query + :param concept: a id of concept, here is the uri of concept + :return: the ic value of the concept + """ + if concept in self._graph_ic: + return self._graph_ic[concept] + else: + freq = int(self._graph_stats.concept_freq(concept)) + if freq == 0: + ic = 0.0 + else: + prob = 1.0 * freq / self._N + ic = -math.log(prob) + self.graph_ic_writer(self._ic_file, [{'concept':concept, 'ic':str(ic)}]) + self._graph_ic[concept] = ic + return ic + + def graph_ic_reader(self, filename): + """ + Load the saved IC values + :param filename: the file containing IC values of concepts + :return: a dictionary concept:IC + """ + data = FileIO.read_json_file(filename) + return {d['concept']:float(d['ic']) for d in data} + + def graph_ic_writer(self, filename, data): + """ + Save the ic values for a concept for faster access. + :param filename: + :param data: + :return: + """ + FileIO.append_json_file(filename, data) diff --git a/sematch/semantic/similarity.py b/sematch/semantic/similarity.py index 0465218..88e5289 100644 --- a/sematch/semantic/similarity.py +++ b/sematch/semantic/similarity.py @@ -1,65 +1,34 @@ +#!/usr/bin/python +# -*- coding: utf-8 -*- +# Copyright 2017 Ganggao Zhu- Grupo de Sistemas Inteligentes +# gzhu[at]dit.upm.es +# DIT, UPM +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from nltk.stem import WordNetLemmatizer from nltk.corpus import wordnet as wn from nltk.corpus import wordnet_ic from nltk.corpus.reader.wordnet import information_content -from gensim.models import Word2Vec - -from sematch.nlp import word_tokenize, lemma, porter, lemmatization +from sematch.semantic.sparql import EntityFeatures, StatSPARQL +from sematch.semantic.graph import GraphIC from sematch.utility import FileIO, memoized -from sematch.sparql import StatSPARQL - -from collections import Counter -import math -class GraphIC: - - """ - This class is used to compute graph-based IC in knowledge graph, which is - basically the proportion of instances tagged with a specific concept - """ - def __init__(self, ic_file): - self._ic_file = ic_file - self._graph_ic = self.graph_ic_reader(ic_file) - self._graph_stats = StatSPARQL() - self._N = self._graph_stats.entity_N() - - def concept_ic(self, concept): - """ - Compute the ic value of a concept using sparql query - :param concept: a id of concept, here is the uri of concept - :return: the ic value of the concept - """ - if concept in self._graph_ic: - return self._graph_ic[concept] - else: - freq = int(self._graph_stats.concept_freq(concept)) - if freq == 0: - ic = 0.0 - else: - prob = 1.0 * freq / self._N - ic = -math.log(prob) - self.graph_ic_writer(self._ic_file, [{'concept':concept, 'ic':str(ic)}]) - self._graph_ic[concept] = ic - return ic - - def graph_ic_reader(self, filename): - """ - Load the saved IC values - :param filename: the file containing IC values of concepts - :return: a dictionary concept:IC - """ - data = FileIO.read_json_file(filename) - return {d['concept']:float(d['ic']) for d in data} - - def graph_ic_writer(self, filename, data): - """ - Save the ic values for a concept for faster access. - :param filename: - :param data: - :return: - """ - FileIO.append_json_file(filename, data) +import math +from collections import Counter class ConceptSimilarity: """ @@ -70,33 +39,50 @@ class ConceptSimilarity: """ def __init__(self, taxonomy, ic_file): self._taxonomy = taxonomy + self._concepts = taxonomy._nodes + self._concept2node = taxonomy._node2id + self._label2concepts = {label:self._concepts[i] for i, label in enumerate(taxonomy._labels)} self._graph_ic = GraphIC(ic_file) + def hyponyms(self, concept): + if concept in self._concept2node: + nodes = self._taxonomy.hyponyms(self._concept2node[concept]) + return [self._concepts[n] for n in nodes] + return [] + + def hypernyms(self, concept): + if concept in self._concept2node: + nodes = self._taxonomy.hypernyms(self._concept2node[concept]) + return [self._concepts[n] for n in nodes] + return [] + + def shortest_path_length(self, concept1, concept2): + n1 = self._concept2node[concept1] + n2 = self._concept2node[concept2] + return self._taxonomy.shortest_path_length(n1, n2) + + def depth(self, concept): + if concept == 'root': + return 1 + n = self._concept2node[concept] + return self._taxonomy.depth(n) + + def least_common_subsumer(self, concept1, concept2): + n1 = self._concept2node[concept1] + n2 = self._concept2node[concept2] + n = self._taxonomy.least_common_subsumer(n1, n2) + if n > len(self._concepts): + return 'root' + return self._concepts[n] + def method(self, name): def function(c1, c2): score = getattr(self, name)(c1, c2) return abs(score) return function - def name2uri(self, name): - """ - Get the URI of a node. - :param name: - :return: - """ - c = self.name2concept(name) - return self.concept2uri(c) if c else None - - def concept2uri(self, c): - return self._taxonomy._nodes[c] if c <= len(self._taxonomy._nodes) else None - def name2concept(self, name): - """ - This function maps a string name to a node in taxonomy based on node's labels - :param name: string name of a concept - :return: the node id if contains the named node otherwise None. - """ - return self._taxonomy._label2id[name] if self._taxonomy._label2id.get(name) else None + return self._label2concepts[name] if name in self._label2concepts else [] def concept_ic(self, concept): """ @@ -104,10 +90,10 @@ def concept_ic(self, concept): :param concept: the node id of concept :return: the ic value of concept """ - if concept == self._taxonomy._root: + if concept == 'root': return 0.0 else: - return self._graph_ic.concept_ic(self._taxonomy._nodes[concept]) + return self._graph_ic.concept_ic(concept) @memoized def similarity(self, c1, c2, name='wpath'): @@ -118,6 +104,8 @@ def similarity(self, c1, c2, name='wpath'): :param name: :return: """ + if c1 not in self._concept2node or c2 not in self._concept2node: + return 'link error' return self.method(name)(c1, c2) def path(self, c1, c2): @@ -127,7 +115,7 @@ def path(self, c1, c2): :param c2: :return: similarity score in [0,1] """ - return 1.0/ self._taxonomy.shortest_path_length(c1, c2) + return 1.0/ self.shortest_path_length(c1, c2) def wup(self, c1, c2): """ @@ -136,16 +124,16 @@ def wup(self, c1, c2): :param c2: :return: """ - lcs = self._taxonomy.least_common_subsumer(c1, c2) - depth_c1 = self._taxonomy.depth(c1) - depth_c2 = self._taxonomy.depth(c2) - depth_lcs = self._taxonomy.depth(lcs) + lcs = self.least_common_subsumer(c1, c2) + depth_c1 = self.depth(c1) + depth_c2 = self.depth(c2) + depth_lcs = self.depth(lcs) return 2.0*depth_lcs / (depth_c1 + depth_c2) def li(self, c1, c2, alpha=0.2, beta=0.6): - path = self._taxonomy.shortest_path_length(c1, c2) - 1 - lcs = self._taxonomy.least_common_subsumer(c1, c2) - depth = self._taxonomy.depth(lcs) + path = self.shortest_path_length(c1, c2) - 1 + lcs = self.least_common_subsumer(c1, c2) + depth = self.depth(lcs) # print path, lcs, depth x = math.exp(-alpha * path) y = math.exp(beta * depth) @@ -156,11 +144,11 @@ def li(self, c1, c2, alpha=0.2, beta=0.6): return x * (a / b) def res(self, c1, c2): - lcs = self._taxonomy.least_common_subsumer(c1, c2) + lcs = self.least_common_subsumer(c1, c2) return self.concept_ic(lcs) def lin(self, c1, c2): - lcs = self._taxonomy.least_common_subsumer(c1, c2) + lcs = self.least_common_subsumer(c1, c2) lcs_ic = self.concept_ic(lcs) c1_ic = self.concept_ic(c1) c2_ic = self.concept_ic(c2) @@ -170,7 +158,7 @@ def lin(self, c1, c2): return 2.0 * lcs_ic / combine def jcn(self, c1, c2): - lcs = self._taxonomy.least_common_subsumer(c1, c2) + lcs = self.least_common_subsumer(c1, c2) lcs_ic = self.concept_ic(lcs) c1_ic = self.concept_ic(c1) c2_ic = self.concept_ic(c2) @@ -180,8 +168,8 @@ def jcn(self, c1, c2): return 1.0 / 1 + (c1_ic + c2_ic - lcs_ic) def wpath(self, c1, c2, k=0.8): - lcs = self._taxonomy.least_common_subsumer(c1, c2) - path = self._taxonomy.shortest_path_length(c1, c2) - 1 + lcs = self.least_common_subsumer(c1, c2) + path = self.shortest_path_length(c1, c2) - 1 weight = k ** self.concept_ic(lcs) return 1.0 / (1 + path * weight) @@ -189,22 +177,13 @@ def wpath(self, c1, c2, k=0.8): class WordNetSimilarity: - # wns = WordNetSimilarity() - # print wns.word_similarity('rooster', 'voyage', 'res') - # print wns.word_similarity('rooster', 'voyage', 'li') - # print wns.word_similarity('rooster', 'voyage', 'jcn') - - # print beef, lamb, octopus, shellfish, meat, seafood, food - - # wns = WordNetSimilarity() - # wns.similarity_all_methods(beef, octopus) - # wns.similarity_all_methods(beef, lamb) - # wns.similarity_all_methods(food, coffee) + """Extend the NLTK's WordNet with more similarity metrics, word lemmatization, and multilingual.""" def __init__(self, ic_corpus='brown'): self._ic_corpus = wordnet_ic.ic('ic-brown.dat') if ic_corpus == 'brown' else wordnet_ic.ic('ic-semcor.dat') self._wn_max_depth = 19 self._default_metrics = ['path','lch','wup','li','res','lin','jcn','wpath'] + self._wn_lemma = WordNetLemmatizer() def method(self, name): def function(syn1, syn2): @@ -212,6 +191,15 @@ def function(syn1, syn2): return abs(score) return function + def synset_expand(self, s): + result = [s] + hypos = s.hyponyms() + if not hypos: + return result + for h in hypos: + result.extend(self.synset_expand(h)) + return result + #return all the noun synsets in wordnet def get_all_synsets(self): return wn.all_synsets('n') @@ -240,12 +228,9 @@ def semcor2offset(self, sense): ''' return self.synset2offset(self.semcor2synset(sense)) - def word2synset(self, word): - w = lemma.lemmatize(word) - syns = wn.synsets(w, pos=wn.NOUN) - if not syns: - syns = wn.synsets(porter.stem(w), pos=wn.NOUN) - return syns + def word2synset(self, word, pos=wn.NOUN): + word = self._wn_lemma.lemmatize(word) + return wn.synsets(word, pos) def multilingual2synset(self, word, lang='spa'): """ @@ -261,7 +246,7 @@ def multilingual2synset(self, word, lang='spa'): @memoized - def similarity(self, c1, c2, name='path'): + def similarity(self, c1, c2, name='wpath'): """ Compute semantic similarity between two concepts :param c1: @@ -298,6 +283,7 @@ def best_synset_pair(self, w1, w2, name='wpath'): def word_similarity_all_metrics(self, w1, w2): return {m:self.word_similarity(w1, w2, name=m) for m in self._default_metrics} + @memoized def word_similarity_wpath(self, w1, w2, k): s1 = self.word2synset(w1) s2 = self.word2synset(w2) @@ -361,10 +347,8 @@ def li(self, c1, c2, alpha=0.2,beta=0.6): path = c1.shortest_path_distance(c2) lcs = self.least_common_subsumer(c1, c2) depth = lcs.max_depth() - #print path, lcs, depth x = math.exp(-alpha*path) y = math.exp(beta*depth) - #print y z = math.exp(-beta*depth) a = y - z b = y + z @@ -387,38 +371,12 @@ def jcn(self, c1, c2): c1_ic = self.synset_ic(c1) c2_ic = self.synset_ic(c2) lcs_ic = self.synset_ic(lcs) - #print lcs, lcs_ic, c1_ic, c2_ic diff = c1_ic + c2_ic - 2*lcs_ic return 1.0/(1 + diff) def lin(self, c1, c2): return c1.lin_similarity(c2, self._ic_corpus) - def gloss_overlap(self, c1, c2): - gloss1 = lemmatization(word_tokenize(c1.definition())) - gloss2 = lemmatization(word_tokenize(c2.definition())) - gloss1 = set(map(porter.stem, gloss1)) - gloss2 = set(map(porter.stem, gloss2)) - return len(gloss1.intersection(gloss2)) - - def elesk(self, c1, c2): - ''' - extended lesk algorithm, measuring word overlaps in definitions. - similarity(A,B) = overlap(gloss(A), gloss(B)) - + overlap(gloss(hypo(A)), gloss(B)) - + overlap(gloss(hypo(A)), gloss(hypo(B))) - + overlap(gloss(A), gloss(hypo(B))) - ''' - hypo1 = c1.hyponyms() - hypo2 = c2.hyponyms() - sim = self.gloss_overlap(c1, c2) - for h1 in hypo1: - sim += self.gloss_overlap(h1, c2) - for h2 in hypo2: - sim += self.gloss_overlap(h1, h2) - for h2 in hypo2: - sim += self.gloss_overlap(c1, h2) - return sim class YagoTypeSimilarity(WordNetSimilarity): @@ -522,41 +480,39 @@ def wpath_graph(self, c1, c2, k=0.9): return 1.0 / (1 + path*weight) -class WordVecSimilarity: +class EntitySimilarity: - def __init__(self, vec_file='models/GoogleNews-vectors-negative300.bin', binary=True): - """ + """This class implements entity relatedness using DBpedia links and entity concepts""" - :param vec_file: the file storing vectors - :param binary: if vector are stored in binary. Google news use binary while yelp not - """ - self._wordvec = Word2Vec.load_word2vec_format(FileIO.filename(vec_file), binary=binary) + def __init__(self): + self._features = EntityFeatures() + self._stats = StatSPARQL() + self._yago = YagoTypeSimilarity() - @memoized - def word_similarity(self, w1, w2): - try: - sim = self._wordvec.similarity(w1, w2) - except: + def similarity(self, entity1, entity2): + concepts_1 = self._features.type(entity1) + concepts_1 = [c for c in concepts_1 if c.__contains__('class/yago')] + concepts_2 = self._features.type(entity2) + concepts_2 = [c for c in concepts_2 if c.__contains__('class/yago')] + synsets_1 = [self._yago.yago2synset(c) for c in concepts_1 if self._yago.yago2synset(c)] + synsets_2 = [self._yago.yago2synset(c) for c in concepts_2 if self._yago.yago2synset(c)] + if not synsets_1 or not synsets_2: return 0.0 - return sim - -class TextSimilarity: - - def __init__(self, sim): - self._sim = sim - - def extract_words(self, text): - return lemmatization(word_tokenize(text)) - - def sum_words_similarity(self, words1, words2, method='path'): - return sum([max([self._sim.word_similarity(w1, w2, method) for w2 in words2]) for w1 in words1]) - - def text_similarity(self, t1, t2, method='path'): - words1 = self.extract_words(t1) - words2 = self.extract_words(t2) - N1 = len(words1) - N2 = len(words2) - sum_1 = self.sum_words_similarity(words1, words2, method) / N1 - sum_2 = self.sum_words_similarity(words2, words1, method) / N2 - return (sum_1 + sum_2) / 2.0 + s1,_ = zip(*Counter({s:self._yago.synset_ic(s) for s in synsets_1}).most_common(5)) + s2,_ = zip(*Counter({s:self._yago.synset_ic(s) for s in synsets_2}).most_common(5)) + N1 = len(s1) + N2 = len(s2) + score1 = sum([max([self._yago.similarity(syn1, syn2) for syn2 in s2]) for syn1 in s1]) / N1 + score2 = sum([max([self._yago.similarity(syn1, syn2) for syn1 in s1]) for syn2 in s2]) / N2 + return (score1 + score2) / 2.0 + + def relatedness(self, entity1, entity2): + ab = self._stats.entity_share(entity1, entity2) + if ab == 0: + return 0 + a = self._stats.entity_relation(entity1) + b = self._stats.entity_relation(entity2) + x = math.log(max([a,b])) - math.log(ab) + y = math.log(self._stats.entity_N()) - math.log(min([a,b])) + return x / y diff --git a/sematch/sparql.py b/sematch/semantic/sparql.py similarity index 69% rename from sematch/sparql.py rename to sematch/semantic/sparql.py index 17ed956..b086b10 100644 --- a/sematch/sparql.py +++ b/sematch/semantic/sparql.py @@ -1,30 +1,39 @@ +#!/usr/bin/python +# -*- coding: utf-8 -*- +# Copyright 2017 Ganggao Zhu- Grupo de Sistemas Inteligentes +# gzhu[at]dit.upm.es +# DIT, UPM +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from SPARQLWrapper import SPARQLWrapper, JSON from rdflib import RDF, RDFS, OWL -import requests class BaseSPARQL: - def __init__(self, url="http://dbpedia.org/sparql"): + """This class implements basic sparql patterns.""" + + def __init__(self, url="http://dbpedia.org/sparql", limit=5000): self._url = url self._sparql = SPARQLWrapper(url) self._sparql.setReturnFormat(JSON) - self._tpl = """SELECT DISTINCT %s WHERE {\n\t%s\n}""" - self._count_tpl = """SELECT %s WHERE {\n\t%s\n}""" - - - def request_execution(self, query): - params={ - "default-graph": "http://dbpedia.org", - "query": query, - "debug": "on", - "format": "application/json", - "timeout": 30000, - } - result = requests.get(self._url, params=params) - return result.text - - def execution(self, query): - #print query + self._tpl = """SELECT DISTINCT %s WHERE {\n\t%s\n} LIMIT """ + str(limit) + self._count_tpl = """SELECT %s WHERE {\n\t%s\n} LIMIT """ + str(limit) + self._text_tpl = """SELECT DISTINCT %s, ?label, ?abstract WHERE {\n\t%s\n} LIMIT """ + str(limit) + + def execution(self, query, show_query=False): + if show_query: + print query self._sparql.setQuery(query) results = self._sparql.query().convert() #print results @@ -33,17 +42,30 @@ def execution(self, query): #SELECT DISTINCT ?x WHERE is query line containing variables #{?s ?p ?o . ?s2 ?p2 ?o } is triples line of query - def execution_template(self, variable, query, triples, template): + def execution_template(self, variable, query, triples, template, show_query=False): """retrieve query results from sparql end point""" - return [r[variable]["value"] for r in self.execution(template % (query, triples))] + return [r[variable]["value"] for r in self.execution(template % (query, triples), show_query)] def create_query(self, variable, triples): """create a sparql query in string based on template, variable and triples""" return self._tpl % (self.q_mark(variable), triples) - def resource_query(self, variable, triples): + def resource_query(self, variable, triples, show_query=False): """execute query to return resources""" - return self.execution_template(variable, self.q_mark(variable), triples, self._tpl) + return self.execution_template(variable, self.q_mark(variable), triples, self._tpl, show_query) + + def text_query(self, variable, triples, lang='en', show_query=False): + triples += self.label_triple(variable, lang) + triples += self.abstract_triple(variable, lang) + data = self.execution(self._text_tpl % (self.q_mark(variable), triples), show_query) + result = [] + for d in data: + res = {} + res['uri'] = d[variable]['value'] + res['label'] = d['label']['value'] + res['abstract'] = d['abstract']['value'] + result.append(res) + return result def count_query(self, variable, triples): """execute query to return counts """ @@ -115,10 +137,15 @@ def so_triple(self, s, o, v): """given known subject and object, predicate is a variable""" return v, self.triple(self.uri(s), self.q_mark(v), self.uri(o)) - def label(self, s, lang='en'): - """query the lable of a resource""" - return self.resource_query(*self.new_triple('o', - self.lang_filter('o', lang))(self.sp_triple, s, RDFS.label)) + def label_triple(self, s, lang='en'): + return self.triple(self.q_mark(s), + self.uri(RDFS.label), + self.q_mark('label')) + self.lang_filter('label', lang) + + def abstract_triple(self, s, lang='en'): + return self.triple(self.q_mark(s), + self.uri('http://dbpedia.org/ontology/abstract'), + self.q_mark('abstract')) + self.lang_filter('abstract', lang) def thing(self, s): return self.po_triple(RDF.type, OWL.Thing, s) @@ -139,9 +166,17 @@ def subclass(self, concept): class EntityFeatures(BaseSPARQL): + """This class implements entity feature extraction from DBpedia through SPARQL, + which supports features such as entity types, labels, abstracts, categories""" + def __init__(self): BaseSPARQL.__init__(self) + def label(self, entity, lang='en'): + """query the lable of a resource""" + return self.resource_query(*self.new_triple('o', + self.lang_filter('o', lang))(self.sp_triple, entity, RDFS.label)) + def type(self, entity): return self.resource_query(*self.sp_triple(entity, RDF.type, 'o')) @@ -156,6 +191,7 @@ def abstract(self, entity, lang='en'): def features(self, entity): return { + 'label':self.label(entity)[0], 'type':self.type(entity), 'category':self.category(entity), 'abstract':self.abstract(entity)[0] @@ -163,6 +199,9 @@ def features(self, entity): class StatSPARQL(BaseSPARQL): + """This class implements statistics for concepts and entities using SPARQL. It can be used to + count concept frequency, entity relation frequency from DBpedia.""" + def __init__(self): BaseSPARQL.__init__(self) @@ -197,20 +236,33 @@ def concept_relation(self, concept): count_2 = self.count_query('p', t1 + t2 + t4) return int(count_1) + int(count_2) - # select count(?p) where s ?p ?o or ?o ?p s + # select count(?o) where s ?p ?o or ?o ?p s # subject is known def entity_relation(self, entity): s1, t1 = self.thing('s1') t2 = self.s_triple(entity, 'p', s1) t3 = self.o_triple(s1, 'p', entity) - count_1 = self.count_query('p', t1 + t2) - count_2 = self.count_query('p', t1 + t3) + count_1 = self.count_query(s1, t1 + t2) + count_2 = self.count_query(s1, t1 + t3) return int(count_1) + int(count_2) + def entity_share(self, entity1, entity2): + s1, t1 = self.thing('s1') + t2 = self.s_triple(entity1, 'p1', s1 ) + t3 = self.o_triple(s1, 'p1', entity1) + t4 = self.s_triple(entity2, 'p2', s1) + t5 = self.o_triple(s1, 'p2', entity2) + count_1 = self.count_query(s1, t1+t2+t4) + count_2 = self.count_query(s1, t1+t2+t5) + count_3 = self.count_query(s1, t1+t3+t4) + count_4 = self.count_query(s1, t1+t3+t5) + return sum(map(int, [count_1, count_2, count_3, count_4])) + class NameSPARQL(BaseSPARQL): - """query resource through names""" + """This class implements exact name matching for entities. It queries entity resource + from DBpedia through entity's names""" def __init__(self): BaseSPARQL.__init__(self) @@ -271,10 +323,19 @@ def name2entities_expand(self, name): class QueryGraph(BaseSPARQL): - def __init__(self): - BaseSPARQL.__init__(self) + """This class implement basic query graph patterns which can query type of things, and a type entity pattern.""" + + def __init__(self, result_limit=5000): + BaseSPARQL.__init__(self, limit=result_limit) + + def type_query(self, concepts, lang='en', show_query=False): + triples = map(lambda x: self.po_triple(RDF.type, x, 's'), concepts) + t1 = [t for v, t in triples] + t1 = self.union(t1) + v, t2 = self.thing('s') + return self.text_query(v, t1 + t2, lang, show_query) - def type_entity_query(self, concepts, entity): + def type_entity_query(self, concepts, entity, show_query=False): """ Construct type entity queries. Some more possible patterns. @@ -291,6 +352,6 @@ def type_entity_query(self, concepts, entity): t1 = self.union(t1) t2 = self.s_triple(entity, 'p', 's') t3 = self.o_triple('s', 'p', entity) - res1 = self.resource_query('s', t1 + t2) - res2 = self.resource_query('s', t1 + t3) + res1 = self.text_query('s', t1 + t2, show_query) + res2 = self.text_query('s', t1 + t3, show_query) return res1 + res2 diff --git a/sematch/server.py b/sematch/server.py deleted file mode 100644 index a7d3723..0000000 --- a/sematch/server.py +++ /dev/null @@ -1,59 +0,0 @@ -from flask import Flask, jsonify, json, request, render_template as template -from QueryEngine import Engine -import os - -DEBUG = True -SECRET_KEY = 'Secret_development_key' - -app = Flask(__name__) -app.config.from_object(__name__) - -engine = Engine() - -@app.route('/api/queries') -def queries(): - query = request.args.get('query') - return json.dumps(engine.type_entity_query_construction(query)) - -@app.route('/api/types') -def types(): - query = request.args.get('query') - return json.dumps(engine.types(query)) - -@app.route('/api/type_search') -def type_search(): - type = request.args.get('type') - sim = request.args.get('sim') - th = request.args.get('th') - result = engine.search_types(type, sim, float(th)) - return json.dumps(result) - -@app.route('/api/entities') -def entities(): - query = request.args.get('query') - return json.dumps(engine.entities(query)) - -@app.route('/api/search') -def search(): - query = request.args.get('query') - results = engine.search(query) - relation_dict = {} - for res in results: - if res['relation'] in relation_dict: - relation_dict[res['relation']].append(res) - else: - relation_dict[res['relation']] =[] - relation_dict[res['relation']].append(res) - return json.dumps([{'relation':key, 'entities':relation_dict[key]} for key in relation_dict.keys()]) - -@app.route('/') -def home(): - return template('demo.html') - -def runserver(): - port = int(os.environ.get('PORT', 5005)) - app.run(host='demos.gsi.dit.upm.es', port=port) - #app.run(host='localhost', port=port) - -if __name__ == '__main__': - runserver() diff --git a/sematch/static/Blob.js b/sematch/static/Blob.js deleted file mode 100644 index 4560b0c..0000000 --- a/sematch/static/Blob.js +++ /dev/null @@ -1,177 +0,0 @@ -/* Blob.js - * A Blob implementation. - * 2013-01-23 - * - * By Eli Grey, http://eligrey.com - * By Devin Samarin, https://github.com/eboyjr - * License: X11/MIT - * See LICENSE.md - */ - -/*global self, unescape */ -/*jslint bitwise: true, regexp: true, confusion: true, es5: true, vars: true, white: true, - plusplus: true */ - -/*! @source http://purl.eligrey.com/github/Blob.js/blob/master/Blob.js */ - -if (typeof Blob !== "function") -var Blob = (function (view) { - "use strict"; - - var BlobBuilder = view.BlobBuilder || view.WebKitBlobBuilder || view.MozBlobBuilder || view.MSBlobBuilder || (function(view) { - var - get_class = function(object) { - return Object.prototype.toString.call(object).match(/^\[object\s(.*)\]$/)[1]; - } - , FakeBlobBuilder = function BlobBuilder() { - this.data = []; - } - , FakeBlob = function Blob(data, type, encoding) { - this.data = data; - this.size = data.length; - this.type = type; - this.encoding = encoding; - } - , FBB_proto = FakeBlobBuilder.prototype - , FB_proto = FakeBlob.prototype - , FileReaderSync = view.FileReaderSync - , FileException = function(type) { - this.code = this[this.name = type]; - } - , file_ex_codes = ( - "NOT_FOUND_ERR SECURITY_ERR ABORT_ERR NOT_READABLE_ERR ENCODING_ERR " - + "NO_MODIFICATION_ALLOWED_ERR INVALID_STATE_ERR SYNTAX_ERR" - ).split(" ") - , file_ex_code = file_ex_codes.length - , real_URL = view.URL || view.webkitURL || view - , real_create_object_URL = real_URL.createObjectURL - , real_revoke_object_URL = real_URL.revokeObjectURL - , URL = real_URL - , btoa = view.btoa - , atob = view.atob - , can_apply_typed_arrays = false - , can_apply_typed_arrays_test = function(pass) { - can_apply_typed_arrays = !pass; - } - - , ArrayBuffer = view.ArrayBuffer - , Uint8Array = view.Uint8Array - ; - FakeBlob.fake = FB_proto.fake = true; - while (file_ex_code--) { - FileException.prototype[file_ex_codes[file_ex_code]] = file_ex_code + 1; - } - try { - if (Uint8Array) { - can_apply_typed_arrays_test.apply(0, new Uint8Array(1)); - } - } catch (ex) {} - if (!real_URL.createObjectURL) { - URL = view.URL = {}; - } - URL.createObjectURL = function(blob) { - var - type = blob.type - , data_URI_header - ; - if (type === null) { - type = "application/octet-stream"; - } - if (blob instanceof FakeBlob) { - data_URI_header = "data:" + type; - if (blob.encoding === "base64") { - return data_URI_header + ";base64," + blob.data; - } else if (blob.encoding === "URI") { - return data_URI_header + "," + decodeURIComponent(blob.data); - } if (btoa) { - return data_URI_header + ";base64," + btoa(blob.data); - } else { - return data_URI_header + "," + encodeURIComponent(blob.data); - } - } else if (real_create_object_URL) { - return real_create_object_URL.call(real_URL, blob); - } - }; - URL.revokeObjectURL = function(object_URL) { - if (object_URL.substring(0, 5) !== "data:" && real_revoke_object_URL) { - real_revoke_object_URL.call(real_URL, object_URL); - } - }; - FBB_proto.append = function(data/*, endings*/) { - var bb = this.data; - // decode data to a binary string - if (Uint8Array && data instanceof ArrayBuffer) { - if (can_apply_typed_arrays) { - bb.push(String.fromCharCode.apply(String, new Uint8Array(data))); - } else { - var - str = "" - , buf = new Uint8Array(data) - , i = 0 - , buf_len = buf.length - ; - for (; i < buf_len; i++) { - str += String.fromCharCode(buf[i]); - } - } - } else if (get_class(data) === "Blob" || get_class(data) === "File") { - if (FileReaderSync) { - var fr = new FileReaderSync; - bb.push(fr.readAsBinaryString(data)); - } else { - // async FileReader won't work as BlobBuilder is sync - throw new FileException("NOT_READABLE_ERR"); - } - } else if (data instanceof FakeBlob) { - if (data.encoding === "base64" && atob) { - bb.push(atob(data.data)); - } else if (data.encoding === "URI") { - bb.push(decodeURIComponent(data.data)); - } else if (data.encoding === "raw") { - bb.push(data.data); - } - } else { - if (typeof data !== "string") { - data += ""; // convert unsupported types to strings - } - // decode UTF-16 to binary string - bb.push(unescape(encodeURIComponent(data))); - } - }; - FBB_proto.getBlob = function(type) { - if (!arguments.length) { - type = null; - } - return new FakeBlob(this.data.join(""), type, "raw"); - }; - FBB_proto.toString = function() { - return "[object BlobBuilder]"; - }; - FB_proto.slice = function(start, end, type) { - var args = arguments.length; - if (args < 3) { - type = null; - } - return new FakeBlob( - this.data.slice(start, args > 1 ? end : this.data.length) - , type - , this.encoding - ); - }; - FB_proto.toString = function() { - return "[object Blob]"; - }; - return FakeBlobBuilder; - }(view)); - - return function Blob(blobParts, options) { - var type = options ? (options.type || "") : ""; - var builder = new BlobBuilder(); - if (blobParts) { - for (var i = 0, len = blobParts.length; i < len; i++) { - builder.append(blobParts[i]); - } - } - return builder.getBlob(type); - }; -}(self)); diff --git a/sematch/static/FileSaver.js b/sematch/static/FileSaver.js deleted file mode 100644 index 6e1fc3c..0000000 --- a/sematch/static/FileSaver.js +++ /dev/null @@ -1,241 +0,0 @@ -/* FileSaver.js - * A saveAs() FileSaver implementation. - * 2014-05-27 - * - * By Eli Grey, http://eligrey.com - * License: X11/MIT - * See https://github.com/eligrey/FileSaver.js/blob/master/LICENSE.md - */ - -/*global self */ -/*jslint bitwise: true, indent: 4, laxbreak: true, laxcomma: true, smarttabs: true, plusplus: true */ - -/*! @source http://purl.eligrey.com/github/FileSaver.js/blob/master/FileSaver.js */ - -var saveAs = saveAs - // IE 10+ (native saveAs) - || (typeof navigator !== "undefined" && - navigator.msSaveOrOpenBlob && navigator.msSaveOrOpenBlob.bind(navigator)) - // Everyone else - || (function(view) { - "use strict"; - // IE <10 is explicitly unsupported - if (typeof navigator !== "undefined" && - /MSIE [1-9]\./.test(navigator.userAgent)) { - return; - } - var - doc = view.document - // only get URL when necessary in case Blob.js hasn't overridden it yet - , get_URL = function() { - return view.URL || view.webkitURL || view; - } - , save_link = doc.createElementNS("http://www.w3.org/1999/xhtml", "a") - , can_use_save_link = !view.externalHost && "download" in save_link - , click = function(node) { - var event = doc.createEvent("MouseEvents"); - event.initMouseEvent( - "click", true, false, view, 0, 0, 0, 0, 0 - , false, false, false, false, 0, null - ); - node.dispatchEvent(event); - } - , webkit_req_fs = view.webkitRequestFileSystem - , req_fs = view.requestFileSystem || webkit_req_fs || view.mozRequestFileSystem - , throw_outside = function(ex) { - (view.setImmediate || view.setTimeout)(function() { - throw ex; - }, 0); - } - , force_saveable_type = "application/octet-stream" - , fs_min_size = 0 - , deletion_queue = [] - , process_deletion_queue = function() { - var i = deletion_queue.length; - while (i--) { - var file = deletion_queue[i]; - if (typeof file === "string") { // file is an object URL - get_URL().revokeObjectURL(file); - } else { // file is a File - file.remove(); - } - } - deletion_queue.length = 0; // clear queue - } - , dispatch = function(filesaver, event_types, event) { - event_types = [].concat(event_types); - var i = event_types.length; - while (i--) { - var listener = filesaver["on" + event_types[i]]; - if (typeof listener === "function") { - try { - listener.call(filesaver, event || filesaver); - } catch (ex) { - throw_outside(ex); - } - } - } - } - , FileSaver = function(blob, name) { - // First try a.download, then web filesystem, then object URLs - var - filesaver = this - , type = blob.type - , blob_changed = false - , object_url - , target_view - , get_object_url = function() { - var object_url = get_URL().createObjectURL(blob); - deletion_queue.push(object_url); - return object_url; - } - , dispatch_all = function() { - dispatch(filesaver, "writestart progress write writeend".split(" ")); - } - // on any filesys errors revert to saving with object URLs - , fs_error = function() { - // don't create more object URLs than needed - if (blob_changed || !object_url) { - object_url = get_object_url(blob); - } - if (target_view) { - target_view.location.href = object_url; - } else { - window.open(object_url, "_blank"); - } - filesaver.readyState = filesaver.DONE; - dispatch_all(); - } - , abortable = function(func) { - return function() { - if (filesaver.readyState !== filesaver.DONE) { - return func.apply(this, arguments); - } - }; - } - , create_if_not_found = {create: true, exclusive: false} - , slice - ; - filesaver.readyState = filesaver.INIT; - if (!name) { - name = "download"; - } - if (can_use_save_link) { - object_url = get_object_url(blob); - save_link.href = object_url; - save_link.download = name; - click(save_link); - filesaver.readyState = filesaver.DONE; - dispatch_all(); - return; - } - // Object and web filesystem URLs have a problem saving in Google Chrome when - // viewed in a tab, so I force save with application/octet-stream - // http://code.google.com/p/chromium/issues/detail?id=91158 - if (view.chrome && type && type !== force_saveable_type) { - slice = blob.slice || blob.webkitSlice; - blob = slice.call(blob, 0, blob.size, force_saveable_type); - blob_changed = true; - } - // Since I can't be sure that the guessed media type will trigger a download - // in WebKit, I append .download to the filename. - // https://bugs.webkit.org/show_bug.cgi?id=65440 - if (webkit_req_fs && name !== "download") { - name += ".download"; - } - if (type === force_saveable_type || webkit_req_fs) { - target_view = view; - } - if (!req_fs) { - fs_error(); - return; - } - fs_min_size += blob.size; - req_fs(view.TEMPORARY, fs_min_size, abortable(function(fs) { - fs.root.getDirectory("saved", create_if_not_found, abortable(function(dir) { - var save = function() { - dir.getFile(name, create_if_not_found, abortable(function(file) { - file.createWriter(abortable(function(writer) { - writer.onwriteend = function(event) { - target_view.location.href = file.toURL(); - deletion_queue.push(file); - filesaver.readyState = filesaver.DONE; - dispatch(filesaver, "writeend", event); - }; - writer.onerror = function() { - var error = writer.error; - if (error.code !== error.ABORT_ERR) { - fs_error(); - } - }; - "writestart progress write abort".split(" ").forEach(function(event) { - writer["on" + event] = filesaver["on" + event]; - }); - writer.write(blob); - filesaver.abort = function() { - writer.abort(); - filesaver.readyState = filesaver.DONE; - }; - filesaver.readyState = filesaver.WRITING; - }), fs_error); - }), fs_error); - }; - dir.getFile(name, {create: false}, abortable(function(file) { - // delete file if it already exists - file.remove(); - save(); - }), abortable(function(ex) { - if (ex.code === ex.NOT_FOUND_ERR) { - save(); - } else { - fs_error(); - } - })); - }), fs_error); - }), fs_error); - } - , FS_proto = FileSaver.prototype - , saveAs = function(blob, name) { - return new FileSaver(blob, name); - } - ; - FS_proto.abort = function() { - var filesaver = this; - filesaver.readyState = filesaver.DONE; - dispatch(filesaver, "abort"); - }; - FS_proto.readyState = FS_proto.INIT = 0; - FS_proto.WRITING = 1; - FS_proto.DONE = 2; 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