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    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
The Naïve Bayes Model for Unsupervised Word Sense Disambiguation: Aspects Concerning Feature Selection

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

T. Hristea, Florentina

Paperback

Series: Springerbriefs in Statistics

General ComputersProbability & Statistics

ISBN10: 3642336922
ISBN13: 9783642336928
Publisher: Springer Nature
Published: Nov 8 2012
Pages: 70
Weight: 0.32
Height: 0.21 Width: 6.14 Depth: 9.21
Language: English

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.

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