@inproceedings{glenski-etal-2021-improving,
    title = "Improving Synonym Recommendation Using Sentence Context",
    author = "Glenski, Maria  and
      Sealy, William I.  and
      Miller, Kate  and
      Arendt, Dustin",
    editor = "Moosavi, Nafise Sadat  and
      Gurevych, Iryna  and
      Fan, Angela  and
      Wolf, Thomas  and
      Hou, Yufang  and
      Marasovi{\'c}, Ana  and
      Ravi, Sujith",
    booktitle = "Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Virtual",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.sustainlp-1.9/",
    doi = "10.18653/v1/2021.sustainlp-1.9",
    pages = "74--78",
    abstract = "Traditional synonym recommendations often include ill-suited suggestions for writer`s specific contexts. We propose a simple approach for contextual synonym recommendation by combining existing human-curated thesauri, e.g. WordNet, with pre-trained language models. We evaluate our technique by curating a set of word-sentence pairs balanced across corpora and parts of speech, then annotating each word-sentence pair with the contextually appropriate set of synonyms. We found that basic language model approaches have higher precision. Approaches leveraging sentence context have higher recall. Overall, the latter contextual approach had the highest F-score."
}