Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET)
This work addresses a core problem in automated lexical resource construction for natural language processing, but it appears incremental as it builds on existing methods with modest performance gains.
The paper tackled the challenge of distinguishing antonyms from synonyms in lexico-semantic analysis by proposing ICE-NET, which models relation-specific properties to enhance classification, achieving up to a 1.8% relative improvement in F1-measure on benchmark datasets.
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. We release the codes for ICE-NET at https://github.com/asif6827/ICENET.