French Verbs Transformation

transformations/french_synonym_verbs_transformation/README.md

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+# Verb Synonym Substitution 🦎  + ⌨️ → 🐍
+
+
+This transformation change some words with synonyms according to if their POS tag is a VERB for simple french sentences. It requires Spacy_lefff (an extention of spacy for french POS and lemmatizing) and nltk package with the open multilingual wordnet dictionary.
+
+Authors : Lisa Barthe and Louanes Hamla from Fablab by Inetum in Paris
+
+## What type of transformation it is ?
+This transformation allows to create paraphrases with a different word in french. The general meaning of the sentence remains but it can be declined on different paraphrases with one verb variation.
+
+## Supported Task
+
+This perturbation can be used for any French task.
+
+## What does it intend to benefit ?
+
+This perturbation would benefit all tasks which have a sentence/paragraph/document as input like text classification, text generation, etc. that requires synthetic data augmentation / diversification.
+
+## What are the limitation of this transformation ?
+This tool does not take the general context into account, sometimes, the ouput will not match the general sense of te sentence.

transformations/french_synonym_verbs_transformation/__init__.py

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+from .transformation import *
+

transformations/french_synonym_verbs_transformation/test.json

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+{
+  "type": "french_verbs_transformation",
+  "test_cases": [
+
+    {
+      "class": "FrenchVerbsSynonymTransformation",
+      "inputs": {
+        "sentence": "je vais finir ce devoir avant demain"
+      },
+      "outputs": [{
+        "sentence": "je vais terminer ce devoir avant demain"
+      }]
+
+    },
+
+    {
+      "class": "FrenchVerbsSynonymTransformation",
+      "inputs": {
+        "sentence": "Puis-je entrer ? Cela fait 10 minutes que je suis en face."
+      },
+      "outputs": [{
+        "sentence": "Puis-je venir ? Cela fait 10 minutes que je suis en face."
+      }]
+
+    },
+
+    {
+      "class": "FrenchVerbsSynonymTransformation",
+      "inputs": {
+        "sentence": "Les psychologues vont devoir calmer les tensions"
+      },
+      "outputs": [{
+        "sentence": "Les psychologues vont devoir soulager les tensions"
+      }]
+
+    },
+
+
+    {
+      "class": "FrenchVerbsSynonymTransformation",
+      "inputs": {
+        "sentence": "J'ai enfin pu faire remorquer la voiture !"
+      },
+      "outputs": [{
+        "sentence": "J'ai enfin pu faire rouler la voiture !"
+      }]
+
+    }
+
+
+  ]
+}

transformations/french_synonym_verbs_transformation/transformation.py

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+from textblob import TextBlob, Blobber, Word
+import re
+from textblob_fr import PatternTagger, PatternAnalyzer
+import nltk
+nltk.download('wordnet')
+from textblob.wordnet import NOUN, VERB, ADV, ADJ
+import spacy
+from spacy_lefff import LefffLemmatizer, POSTagger
+from spacy.language import Language
+from nltk.corpus import wordnet
+import nltk
+nltk.download('omw') 
+
+from interfaces.SentenceOperation import SentenceOperation
+from tasks.TaskTypes import TaskType
+
+@Language.factory('french_lemmatizer')
+def create_french_lemmatizer(nlp, name):
+    return LefffLemmatizer()
+
+@Language.factory('POSTagger')
+def create_POSTagger(nlp, name):
+    return POSTagger()
+
+
+nlp = spacy.load('fr_core_news_md')
+
+nlp.add_pipe('POSTagger', name ='pos')
+nlp.add_pipe('french_lemmatizer', name='lefff', after='pos')
+
+def synonym_transformation(text):    
+	doc = nlp(text)
+	verbs = [d.text for d in doc if d.pos_ == "VERB"]
+	synonyms_verb_list = []
+	for i in verbs :
+		dict_verb_synonyms = {}
+		dict_verb_synonyms['verb'] = i
+		dict_verb_synonyms['synonyms'] = list(set([l.name() for syn in wordnet.synsets(i, lang = 'fra', pos = VERB) for l in syn.lemmas('fra')]))
+		if len(dict_verb_synonyms['synonyms']) > 0:
+			synonyms_verb_list.append(dict_verb_synonyms)
+	valid_verb_list = []
+	for j in synonyms_verb_list:
+		for k in j['synonyms']:
+			valid_verb_dict = {}
+			valid_verb_dict['verb'] = j['verb']
+			valid_verb_dict['syn'] = k
+			if nlp(j['verb']).similarity(nlp(k)) > .60 and not nlp(j['verb']).similarity(nlp(k)) >= .999:
+				valid_verb_list.append(valid_verb_dict)
+	text_verb_generated = []
+	pertu=[]
+	for l in valid_verb_list:
+		text_verb_generated.append(text.replace(l['verb'], l['syn']))
+	text_verb_generated.sort(reverse=True)
+	for sent in text_verb_generated:
+		if nlp(text).similarity(nlp(i)) > .10 and not nlp(text).similarity(nlp(i)) >= .999:
+			pertu.append(sent)
+			break
+
+	return pertu
+
+
+
+
+
+class FrenchVerbsSynonymTransformation(SentenceOperation):
+    tasks = [
+        TaskType.TEXT_CLASSIFICATION,
+        TaskType.TEXT_TO_TEXT_GENERATION,
+        TaskType.TEXT_TAGGING,
+    ]
+    languages = ["fr"]
+
+    def __init__(self, seed=0, max_outputs=1):
+        super().__init__(seed, max_outputs=max_outputs)
+
+    def generate(self, sentence : str):
+        perturbed_texts = synonym_transformation(
+            sentence
+        )
+        print("perturbed text inside of class",perturbed_texts)
+        return perturbed_texts
+
+