Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network
Kim-Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu · 2017
Distinguishing between antonyms and synonyms is a key task to achieve high performance in NLP systems.While they are notoriously difficult to distinguish by distributional co-occurrence models, pattern-based methods have proven effective to differentiate between the relations.In this paper, we present a novel neural network model AntSynNET that exploits lexico-syntactic patterns from syntactic parse trees.In addition to the lexical and syntactic information, we successfully integrate the distance between the related words along the syntactic path as a new pattern feature.The results from classification experiments show that AntSyn-NET improves the performance over prior pattern-based methods.