Latent Semantic Word Sense Disambiguation Using Global Co-occurrence Information

TL;DR

This paper introduces a novel word sense disambiguation approach leveraging global co-occurrence data and NMF to address data sparsity, demonstrating improved effectiveness over baseline methods.

Contribution

It proposes using co-occurrence frequencies of dependency relations across the entire training set to enhance NMF-based disambiguation, reducing data sparsity issues.

Findings

The method outperforms baseline approaches in accuracy.

Using global co-occurrence improves stability of disambiguation.

The approach effectively mitigates data sparsity problems.

Abstract

In this paper, I propose a novel word sense disambiguation method based on the global co-occurrence information using NMF. When I calculate the dependency relation matrix, the existing method tends to produce very sparse co-occurrence matrix from a small training set. Therefore, the NMF algorithm sometimes does not converge to desired solutions. To obtain a large number of co-occurrence relations, I propose to use co-occurrence frequencies of dependency relations between word features in the whole training set. This enables us to solve data sparseness problem and induce more effective latent features. To evaluate the efficiency of the method of word sense disambiguation, I make some experiments to compare with the result of the two baseline methods. The results of the experiments show this method is effective for word sense disambiguation in comparison with the all baseline methods. Moreover, the proposed method is effective for obtaining a stable effect by analyzing the global co-occurrence information.