The Naïve Bayes Model in the Context of Word Sense Disambiguation
This chapter discusses the Naïve Bayes model strictly in the context of word sense disambiguation. The theoretical model is presented and its implementation is discussed. Special attention is paid to parameter estimation and to feature selection, the two main issues of the model’s implementation. The EM algorithm is recommended as suitable for parameter estimation in the case of unsupervised WSD. Feature selection will be surveyed in the following chapters.
KeywordsBayesian classification Expectation-Maximization algorithm Naïve Bayes classifier
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