Improving Synonym Recommendation Using Sentence Context

Maria Glenski, William I. Sealy, Kate Miller, Dustin Arendt


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
Venues:
EMNLP | 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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2021.sustainlp-1.9.pdf