  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.
