Feeding Syntactic Versus Semantic Knowledge to a Knowledge-lean Unsupervised Word Sense Disambiguation Algorithm with an Underlying Naive Bayes Model

• Autorzy: Hristea F. , Colhon M.
• Języki publikacji: EN

Abstrakty

EN

The present paper concentrates on the issue of feature selection for unsupervised word sense disambiguation (WSD) performed with an underlying Na¨ýve Bayes model. It introduces dependency-based feature selection which, to our knowledge, is used for the first time in conjunction with the Na¨ýve Bayes model acting as clustering technique. Construction of the dependency-based semantic space required for the proposed task is discussed. The resulting disambiguation method, representing an extension of the method introduced in [15], lies at the border between unsupervised and knowledge-based techniques. Syntactic knowledge provided by dependency relations (and exemplified in the case of adjectives) is hereby compared to semantic knowledge offered by the semantic network WordNet (and examined in [15]). Our conclusion is that the Na¨ýve Bayes model reacts well in the presence of syntactic knowledge of this type and that dependency-based feature selection is a reliable alternative to the WordNet-based semantic one.

Słowa kluczowe

EN

unsupervised word sense disambiguation; Bayesian classification; EM algorithm; WordNet; dependency relations; dependency-based feature selection

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2012
• Tom: Vol. 119, nr 1
• Strony: 61--86
• Opis fizyczny: Bibliogr. 44 poz., tab.

Twórcy

autor

Hristea F.

autor

Colhon M.

• Department of Computer Science, Faculty of Mathematics and Computer Science, University of Bucharest, 14, Academiei Str., Bucharest, Sector 1, C.P. 010014, Romania,

Bibliografia

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