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The Naïve Bayes Model in the Context of Word Sense Disambiguation

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The Naïve Bayes Model for Unsupervised Word Sense Disambiguation

Part of the book series: SpringerBriefs in Statistics ((BRIEFSSTATIST))

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Abstract

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.

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Notes

  1. 1.

    A bag is similar to a set, only it allows repetition.

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Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Department of Computer Science, University of Bucharest, Emil Racovita 12, 041758, Bucharest, Romania

    Florentina T. Hristea

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  1. Florentina T. Hristea
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Correspondence to Florentina T. Hristea .

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Hristea, F.T. (2013). The Naïve Bayes Model in the Context of Word Sense Disambiguation. In: The Naïve Bayes Model for Unsupervised Word Sense Disambiguation. SpringerBriefs in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33693-5_2

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