Syntactic Dependency-Based Feature Selection

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

Abstract

The feature selection method we are presenting in this chapter makes use of syntactic knowledge provided by dependency relations. Dependency-based feature selection for the Naïve Bayes model is examined and exemplified in the case of adjectives. Performing this type of knowledge-based feature selection places the disambiguation process at the border between unsupervised and knowledge-based techniques. The discussed type of feature selection and corresponding disambiguation method will once again prove that a basic, simple knowledge-lean disambiguation algorithm, hereby represented by the Naïve Bayes model, can perform quite well when provided knowledge in an appropriate way. Our main conclusion will be that the Naïve Bayes model reacts well in the presence of syntactic knowledge of this type and that dependency-based feature selection for the Naïve Bayes model is a reliable alternative to the WordNet-based semantic one.

Keywords

Bayesian classification Word sense disambiguation Unsupervised disambiguation Knowledge-based disambiguation Dependency relations  Dependency-based feature selection 

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Copyright information

© The Author(s) 2013

Authors and Affiliations

  1. 1.Faculty of Mathematics and Computer Science, Department of Computer ScienceUniversity of BucharestBucharestRomania

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