Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network
This addresses a key challenge in NLP for achieving high performance, though it appears incremental as it builds on existing pattern-based approaches.
The paper tackled the problem of distinguishing antonyms and synonyms in NLP by introducing AntSynNET, a neural network that uses lexico-syntactic patterns and a new distance feature, resulting in improved performance over prior pattern-based methods.
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.