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 AntSynNET improves the performance over prior pattern-based methods.
@article{arxiv.1701.02962,
title = {Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network},
author = {Kim Anh Nguyen and Sabine Schulte im Walde and Ngoc Thang Vu},
journal= {arXiv preprint arXiv:1701.02962},
year = {2017}
}