By Florentina T. Hristea

This publication offers contemporary advances (from 2008 to 2012) touching on use of the Naïve Bayes version in unsupervised observe experience disambiguation (WSD).

While WSD, regularly, has a few vital functions in a number of fields of synthetic intelligence (information retrieval, textual content processing, desktop translation, message figuring out, man-machine communique etc.), unsupervised WSD is taken into account very important since it is language-independent and doesn't require formerly annotated corpora. The Naïve Bayes version has been general in supervised WSD, yet its use in unsupervised WSD has ended in extra modest disambiguation effects and has been much less widespread. apparently the opportunity of this statistical version with admire to unsupervised WSD keeps to stay insufficiently explored.

The current booklet contends that the Naïve Bayes version should be fed wisdom with a purpose to practice good as a clustering strategy for unsupervised WSD and examines 3 totally varied assets of such wisdom for function choice: WordNet, dependency family members and internet N-grams. WSD with an underlying Naïve Bayes version is eventually situated at the border among unsupervised and knowledge-based strategies. some great benefits of feeding wisdom (of numerous natures) to a knowledge-lean set of rules for unsupervised WSD that makes use of the Naïve Bayes version as clustering process are sincerely highlighted. The dialogue exhibits that the Naïve Bayes version nonetheless holds promise for the open challenge of unsupervised WSD.

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The Naïve Bayes Model for Unsupervised Word Sense Disambiguation: Aspects Concerning Feature Selection (SpringerBriefs in Statistics) by Florentina T. Hristea


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