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The Naïve Bayes Model for Unsupervised Word Sense Disambiguation : Aspects Concerning Feature Selection by Florentina T. Hristea (auth.) is a nonfiction available to read on EtoBox.

What is The Naïve Bayes Model for Unsupervised Word Sense Disambiguation : Aspects Concerning Feature Selection about?

This book presents recent advances (from 2008 to 2012) concerning use of the Naïve Bayes model in unsupervised word sense disambiguation (WSD). While WSD, in general, has a number of important applications in various fields of artificial intelligence (information retrieval, text processing, machine translation, message understanding, man-machine communication etc.), unsupervised WSD is considered important because it is language-independent and does not require previously annotated corpora. The Naïve Bayes model has been widely used in supervised WSD, but its use in unsupervised WSD has led to more modest disambiguation results and has been less frequent. It seems that the potential of this statistical model with respect to unsupervised WSD continues to remain insufficiently explored. The present book contends that the Naïve Bayes model needs to be fed knowledge in order to perform well as a clustering technique for unsupervised WSD and examines three entirely different sources of such knowledge for feature selection: WordNet, dependency relations and web N-grams. WSD with an underlying Naïve Bayes model is ultimately positioned on the border between unsupervised and knowledge-base

Who reads The Naïve Bayes Model for Unsupervised Word Sense Disambiguation : Aspects Concerning Feature Selection?

Author

Florentina T. Hristea (auth.)

Publisher

Springer Berlin Heidelberg : Imprint : Springer

Published

2013

Language

EN

ISBN

9783642336928

Category

nonfiction

Subjects

Science, Computer Science, Mathematics