Naive Bayes and Exemplar-Based approaches to Word Sense Disambiguation Revisited

TL;DR

This paper compares Naive Bayes and Exemplar-based methods for Word Sense Disambiguation, clarifies previous misconceptions, proposes an efficiency improvement, and finds Exemplar-based approaches generally outperform Bayesian ones.

Contribution

It provides a detailed comparison of Naive Bayes and Exemplar-based methods, introduces an efficiency enhancement, and clarifies conflicting literature findings.

Findings

Exemplar-based approach generally outperforms Naive Bayes in WSD.

Using only positive information improves efficiency without accuracy loss.

The proposed method scales well with large attribute sets.

Abstract

This paper describes an experimental comparison between two standard supervised learning methods, namely Naive Bayes and Exemplar-based classification, on the Word Sense Disambiguation (WSD) problem. The aim of the work is twofold. Firstly, it attempts to contribute to clarify some confusing information about the comparison between both methods appearing in the related literature. In doing so, several directions have been explored, including: testing several modifications of the basic learning algorithms and varying the feature space. Secondly, an improvement of both algorithms is proposed, in order to deal with large attribute sets. This modification, which basically consists in using only the positive information appearing in the examples, allows to improve greatly the efficiency of the methods, with no loss in accuracy. The experiments have been performed on the largest sense-tagged corpus available containing the most frequent and ambiguous English words. Results show that the Exemplar-based approach to WSD is generally superior to the Bayesian approach, especially when a specific metric for dealing with symbolic attributes is used.