New PDF release: The Naïve Bayes Model for Unsupervised Word Sense

By Florentina T. Hristea

ISBN-10: 3642336922

ISBN-13: 9783642336928

ISBN-10: 3642336930

ISBN-13: 9783642336935

This e-book offers fresh advances (from 2008 to 2012) relating use of the Naïve Bayes version in unsupervised be aware feel disambiguation (WSD).

While WSD, commonly, has a couple of vital purposes in numerous fields of man-made intelligence (information retrieval, textual content processing, computer translation, message figuring out, man-machine communique etc.), unsupervised WSD is taken into account vital since it is language-independent and doesn't require formerly annotated corpora. The Naïve Bayes version has been commonplace in supervised WSD, yet its use in unsupervised WSD has ended in extra modest disambiguation effects and has been much less common. apparently the possibility of this statistical version with admire to unsupervised WSD keeps to stay insufficiently explored.

The current e-book contends that the Naïve Bayes version has to be fed wisdom for you to practice good as a clustering procedure for unsupervised WSD and examines 3 completely diversified resources of such wisdom for characteristic choice: WordNet, dependency family and internet N-grams. WSD with an underlying Naïve Bayes version is eventually situated at the border among unsupervised and knowledge-based ideas. 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 essentially highlighted. The dialogue indicates that the Naïve Bayes version nonetheless holds promise for the open challenge of unsupervised WSD.

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

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Test results were equally compared to those obtained in (Pedersen and Bruce 1998). Corresponding to verbs the Bruce et al. (1996) data was used as test data once again. From this 12-word sense-tagged corpus the verb help was selected, out of a total of 4 sense-tagged verbs. This choice was again determined by the fact that help is a verb in the case of which disambiguation results are quite modest when using the Pedersen-and-Bruce-type local features. The distribution of senses corresponding to help that has been used This is synset {common} having the gloss ‘belonging to or participated in by a community as a whole; public’.

In: Proceedings of the 15th National Conference on Artificial Intelligence, pp. 800–805. Madison, Wisconsin (1998) Chapter 4 Syntactic Dependency-Based Feature Selection 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.

In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 805–810. : The measure of a model. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 101–112. : Retrieval time from semantic memory. J. Verb. Learn. Verb. Be. 8, 240–247 (1969) Fellbaum, C. ): WordNet: An Electronic Lexical Database. : Recent advances concerning the usage of the Naïve Bayes model in unsupervised word sense disambiguation. Int. Rev. Comput. Softw. : Performing word sense disambiguation at the border between unsupervised and knowledge-based techniques.

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