Abstract
Usage similarity estimation addresses the semantic proximity of word instances in different contexts. We apply contextualized (ELMo and BERT) word and sentence embeddings to this task, and propose supervised models that leverage these representations for prediction. Our models are further assisted by lexical substitute annotations automatically assigned to word instances by context2vec, a neural model that relies on a bidirectional LSTM. We perform an extensive comparison of existing word and sentence representations on benchmark datasets addressing both graded and binary similarity.The best performing models outperform previous methods in both settings.- Anthology ID:
- S19-1002
- Volume:
- Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Venues:
- SemEval | *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9–21
- Language:
- URL:
- https://aclanthology.org/S19-1002
- DOI:
- 10.18653/v1/S19-1002
- Cite (ACL):
- Aina Garí Soler, Marianna Apidianaki, and Alexandre Allauzen. 2019. Word Usage Similarity Estimation with Sentence Representations and Automatic Substitutes. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), pages 9–21, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Word Usage Similarity Estimation with Sentence Representations and Automatic Substitutes (Garí Soler et al., SemEval-*SEM 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/S19-1002.pdf