Abstract
Antonyms vs synonyms distinction is a core challenge in lexico-semantic analysis and automated lexical resource construction. These pairs share a similar distributional context which makes it harder to distinguish them. Leading research in this regard attempts to capture the properties of the relation pairs, i.e., symmetry, transitivity, and trans-transitivity. However, the inability of existing research to appropriately model the relation-specific properties limits their end performance. In this paper, we propose InterlaCed Encoder NETworks (i.e., ICE-NET) for antonym vs synonym distinction, that aim to capture and model the relation-specific properties of the antonyms and synonyms pairs in order to perform the classification task in a performance-enhanced manner. Experimental evaluation using the benchmark datasets shows that ICE-NET outperforms the existing research by a relative score of upto 1.8% in F1-measure.- Anthology ID:
- 2024.findings-eacl.99
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2024
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1462–1473
- Language:
- URL:
- https://aclanthology.org/2024.findings-eacl.99
- DOI:
- Cite (ACL):
- Muhammad Ali, Yan Hu, Jianbin Qin, and Di Wang. 2024. Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET). In Findings of the Association for Computational Linguistics: EACL 2024, pages 1462–1473, St. Julian’s, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET) (Ali et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2024.findings-eacl.99.pdf