A simple approach to building ensembles of Naive Bayesian classifiers for word sense disambiguation

Authors:
Ted Pedersen
Affiliations:
University of Minnesota Duluth, Duluth, MN
Venue:
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Year:
2000

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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.