Antonym vs Synonym Distinction using InterlaCed Encoder NETworks
  (ICE-NET)
Muhammad Asif Ali Yan Hu Jianbin Qin Di Wang
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.