An Ensemble Approach to Corpus Based Word Sense Disambiguation

This paper presents a corpus{based approach to word sense disambiguation that combines a number of Naive Bayesian classiers into an ensemble that performs disambiguation via a majority vote. Each of the member classiers is based on collocation and co{occurrence features found in varying sized windows of context. This approach is motivated by the observation that, in general, enhancing the feature set or learning algorithm used by a corpus{ based approach does not improve disambiguation accuracy beyond what can be attained with shallow lexical features and the Naive Bayesian classier. Despite the simplicity of this approach, empirical results disambiguating the widely studied nouns line and interest show that such an ensemble achieves levels of accuracy comparable to the best previously published results.