Network Features Based Co-hyponymy Detection

TL;DR

This paper introduces a supervised network-based model for detecting co-hyponymy relations in lexical semantics, achieving high accuracy and competitive performance compared to existing methods.

Contribution

It proposes a novel supervised approach utilizing network measures for co-hyponymy detection, an area less explored compared to hypernymy detection.

Findings

Achieves high accuracy in co-hyponymy detection

Performs better or at par with state-of-the-art models

Introduces network measures as effective features

Abstract

Distinguishing lexical relations has been a long term pursuit in natural language processing (NLP) domain. Recently, in order to detect lexical relations like hypernymy, meronymy, co-hyponymy etc., distributional semantic models are being used extensively in some form or the other. Even though a lot of efforts have been made for detecting hypernymy relation, the problem of co-hyponymy detection has been rarely investigated. In this paper, we are proposing a novel supervised model where various network measures have been utilized to identify co-hyponymy relation with high accuracy performing better or at par with the state-of-the-art models.