Discriminating word senses with tourist walks in complex networks

TL;DR

This paper introduces a novel approach using deterministic tourist walks in complex networks to improve word sense disambiguation, outperforming traditional network measurements in accuracy.

Contribution

It is the first application of deterministic tourist walks to word sense disambiguation, demonstrating improved accuracy over traditional network-based methods.

Findings

Tourist walk characterization enhances disambiguation accuracy.

Deterministic walks outperform traditional network measurements.

Method shows potential for broader applications in related fields.

Abstract

Patterns of topological arrangement are widely used for both animal and human brains in the learning process. Nevertheless, automatic learning techniques frequently overlook these patterns. In this paper, we apply a learning technique based on the structural organization of the data in the attribute space to the problem of discriminating the senses of 10 polysemous words. Using two types of characterization of meanings, namely semantical and topological approaches, we have observed significative accuracy rates in identifying the suitable meanings in both techniques. Most importantly, we have found that the characterization based on the deterministic tourist walk improves the disambiguation process when one compares with the discrimination achieved with traditional complex networks measurements such as assortativity and clustering coefficient. To our knowledge, this is the first time that such deterministic walk has been applied to such a kind of problem. Therefore, our finding suggests that the tourist walk characterization may be useful in other related applications.