A Simple Approach to Building Ensembles of Naive Bayesian Classifiers for Word Sense Disambiguation

TL;DR

This paper introduces a straightforward ensemble method of Naive Bayesian classifiers using lexical context features for word sense disambiguation, achieving competitive accuracy on common noun datasets.

Contribution

It proposes a simple ensemble approach with lexical features for word sense disambiguation that rivals more complex methods.

Findings

Achieves accuracy comparable to state-of-the-art methods

Uses lexical co-occurrence features within varying context windows

Demonstrates effectiveness on noun disambiguation tasks

Abstract

This paper presents a corpus-based approach to word sense disambiguation that builds an ensemble of Naive Bayesian classifiers, each of which is based on lexical features that represent co--occurring words in varying sized windows of context. Despite the simplicity of this approach, empirical results disambiguating the widely studied nouns line and interest show that such an ensemble achieves accuracy rivaling the best previously published results.