Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET)

Muhammad Ali, Yan Hu, Jianbin Qin, Di Wang


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:
Bibkey:
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)
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PDF:
https://aclanthology.org/2024.findings-eacl.99.pdf