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Antonyms vs synonyms distinction is a core challenge in lexico-semanticanalysis and automated lexical resource construction. These pairs share asimilar distributional context which makes it harder to distinguish them.Leading research in this regard attempts to capture the properties of therelation pairs, i.e., symmetry, transitivity, and trans-transitivity. However,the inability of existing research to appropriately model the relation-specificproperties limits their end performance. In this paper, we propose InterlaCedEncoder NETworks (i.e., ICE-NET) for antonym vs synonym distinction, that aimto capture and model the relation-specific properties of the antonyms andsynonyms pairs in order to perform the classification task in aperformance-enhanced manner. Experimental evaluation using the benchmarkdatasets shows that ICE-NET outperforms the existing research by a relativescore of upto 1.8% in F1-measure. We release the codes for ICE-NET athttps://github.com/asif6827/ICENET.

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