references.bib

@Article{ggstatsplot,
    doi = {10.21105/joss.03167},
    url = {https://doi.org/10.21105/joss.03167},
    year = {2021},
    publisher = {{The Open Journal}},
    volume = {6},
    number = {61},
    pages = {3167},
    author = {Indrajeet Patil},
    title = {{Visualizations with statistical details: The {'ggstatsplot'} approach}},
    journal = {{Journal of Open Source Software}},
  }
@article{allais1953,
	title = {Le comportement de l'homme rationnel devant le risque: Critique des postulats et axiomes de l'ecole Americaine. [Rational man's behavior in the presence of risk: critique of the postulates and axioms of the American school.]},
	author = {Allais, M.},
	year = {1953},
	date = {1953},
	journal = {Econometrica},
	pages = {503--546},
	volume = {21},
	doi = {10.2307/1907921}
}

@article{grootendorst2022bertopic,
  title={BERTopic: Neural topic modeling with a class-based TF-IDF procedure},
  author={Grootendorst, Maarten},
  journal={arXiv preprint arXiv:2203.05794},
  year={2022}
}

@article{qi2020stanza,
  title={Stanza: A Python natural language processing toolkit for many human languages},
  author={Qi, Peng and Zhang, Yuhao and Zhang, Yuhui and Bolton, Jason and Manning, Christopher D},
  journal={arXiv preprint arXiv:2003.07082},
  year={2020}
}

@inproceedings{nguyen2021trankit,
   title={Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing},
   author={Nguyen, Minh Van and Lai, Viet and Veyseh, Amir Pouran Ben and Nguyen, Thien Huu},
   booktitle="Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
   year={2021}
}

@inproceedings{straka-strakova-2017-tokenizing,
    title = "Tokenizing, {POS} Tagging, Lemmatizing and Parsing {UD} 2.0 with {UDP}ipe",
    author = "Straka, Milan  and
      Strakov{\'a}, Jana",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/K17-3009",
    doi = "10.18653/v1/K17-3009",
    pages = "88--99",
    abstract = "Many natural language processing tasks, including the most advanced ones, routinely start by several basic processing steps {--} tokenization and segmentation, most likely also POS tagging and lemmatization, and commonly parsing as well. A multilingual pipeline performing these steps can be trained using the Universal Dependencies project, which contains annotations of the described tasks for 50 languages in the latest release UD 2.0. We present an update to UDPipe, a simple-to-use pipeline processing CoNLL-U version 2.0 files, which performs these tasks for multiple languages without requiring additional external data. We provide models for all 50 languages of UD 2.0, and furthermore, the pipeline can be trained easily using data in CoNLL-U format. UDPipe is a standalone application in C++, with bindings available for Python, Java, C{\#} and Perl. In the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, UDPipe was the eight best system, while achieving low running times and moderately sized models.",
}

@article{DBLP:journals/corr/abs-1906-08237,
  author    = {Zhilin Yang and
               Zihang Dai and
               Yiming Yang and
               Jaime G. Carbonell and
               Ruslan Salakhutdinov and
               Quoc V. Le},
  title     = {XLNet: Generalized Autoregressive Pretraining for Language Understanding},
  journal   = {CoRR},
  volume    = {abs/1906.08237},
  year      = {2019},
  url       = {http://arxiv.org/abs/1906.08237},
  eprinttype = {arXiv},
  eprint    = {1906.08237},
  timestamp = {Mon, 24 Jun 2019 17:28:45 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1906-08237.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{sepehri2022passivepy,
  title={PassivePy: A tool to automatically identify passive voice in big text data},
  author={Sepehri, Amir and Mirshafiee, Mitra Sadat and Markowitz, David M},
  journal={Journal of Consumer Psychology},
  year={2022},
  publisher={Wiley Online Library}
}

@softwareversion{plique:hal-03903518v1,
  TITLE = {{ipysigma}},
  AUTHOR = {Plique, Guillaume},
  URL = {https://sciencespo.hal.science/hal-03903518},
  NOTE = {},
  YEAR = {2022},
  MONTH = Dec,
  DOI = {10.5281/zenodo.7446059},
  SWHID = {swh:1:dir:4e884d22aab6d1561eed1b681425192f4d691596;origin=https://hal.archives-ouvertes.fr/hal-03903518;visit=swh:1:snp:d39c087b585d649d345c7919b6837772977fb3fe;anchor=swh:1:rel:85a3cc872f529c29692b7a19eb6267cbf429c389;path=/},
  VERSION = {0.23.0},
  REPOSITORY = {https://github.com/medialab/ipysigma},
  LICENSE = {MIT License},
  KEYWORDS = {Jupyter ; Python ; Notebook ; Graph theory},
  HAL_ID = {hal-03903518},
  HAL_VERSION = {v1},
}