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Semantic WordNet-Based Feature Selection

  • Florentina T. Hristea
Chapter
Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Abstract

The feature selection method we are presenting in this chapter makes use of the semantic network WordNet as knowledge source for feature selection. The method makes ample use of the WordNet semantic relations which are typical of each part of speech, thus placing the disambiguation process at the border between unsupervised and knowledge-based techniques. Test results corresponding to the main parts of speech (nouns, adjectives, verbs) will be compared to previously existing disambiguation results, obtained when performing a completely different type of feature selection. Our main conclusion will be that the Naïve Bayes model reacts well in the presence of semantic knowledge provided by WN-based feature selection when acting as clustering technique for unsupervised WSD.

Keywords

Bayesian classification Word sense disambiguation  Unsupervised disambiguation Knowledge-based disambiguation  WordNet 

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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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