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.- Anthology ID:
- 2021.sustainlp-1.9
- Volume:
- Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Virtual
- Editors:
- Nafise Sadat Moosavi, Iryna Gurevych, Angela Fan, Thomas Wolf, Yufang Hou, Ana Marasović, Sujith Ravi
- Venue:
- sustainlp
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 74–78
- Language:
- URL:
- https://aclanthology.org/2021.sustainlp-1.9
- DOI:
- 10.18653/v1/2021.sustainlp-1.9
- Cite (ACL):
- Maria Glenski, William I. Sealy, Kate Miller, and Dustin Arendt. 2021. Improving Synonym Recommendation Using Sentence Context. In Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing, pages 74–78, Virtual. Association for Computational Linguistics.
- Cite (Informal):
- Improving Synonym Recommendation Using Sentence Context (Glenski et al., sustainlp 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2021.sustainlp-1.9.pdf