Unveiling the relationship between complex networks metrics and word senses

TL;DR

This paper explores how complex network metrics can be used to improve word sense disambiguation by analyzing local network structures around ambiguous words, showing promising results compared to traditional methods.

Contribution

It introduces a novel approach to WSD using complex network analysis, highlighting the relevance of hierarchical connectivity and clustering features.

Findings

In half of the cases, the approach outperforms traditional shallow methods.

Hierarchical connectivity and clustering are key features for WSD.

Complex network parameters relate to semantic distinctions.

Abstract

The automatic disambiguation of word senses (i.e., the identification of which of the meanings is used in a given context for a word that has multiple meanings) is essential for such applications as machine translation and information retrieval, and represents a key step for developing the so-called Semantic Web. Humans disambiguate words in a straightforward fashion, but this does not apply to computers. In this paper we address the problem of Word Sense Disambiguation (WSD) by treating texts as complex networks, and show that word senses can be distinguished upon characterizing the local structure around ambiguous words. Our goal was not to obtain the best possible disambiguation system, but we nevertheless found that in half of the cases our approach outperforms traditional shallow methods. We show that the hierarchical connectivity and clustering of words are usually the most relevant features for WSD. The results reported here shine light on the relationship between semantic and structural parameters of complex networks. They also indicate that when combined with traditional techniques the complex network approach may be useful to enhance the discrimination of senses in large texts