A neural network model for efficient antonymy-synonymy classification by exploiting co-occurrence contexts and word-structure patterns

Abstract

Antonymy and synonymy are basic semantic relations between words. Automatically distinguishing between antonymy and synonymy is an important task in natural language processing. This task is hard because antonyms and synonyms tend to occur in highly similar contexts. Recent studies often focus on exploiting densevector representations of words to deal with this problem. In this paper, we present a study on antonymy-synonymy discrimination for the Vietnamese language. We propose a deep neural network model (DVASNet) that can utilize not only embedding representations of words but also co-occurrence contexts and specific patterns of Vietnamese word structure. Our experimental results showed that the proposed method achieved significant improvements in comparison with a number of state-of-the-art methods by 14% to 17% in terms of F1 score for both benchmark datasets.