Isbn: 9783642336928 - the naïve bayes model for unsupervised word sense disambiguation: aspects concerning feature selection (springerbriefs in statistics) (16 resultados)

Paperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New. In English.

Condición: New. pp. 86.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: Brand New. 2013 edition. 83 pages. 9.25x6.25x0.25 inches. In Stock.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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-based techniques. The benefits of feeding knowledge (of various natures) to a knowledge-lean algorithm for unsupervised WSD that uses the Naïve Bayes model as clustering technique are clearly highlighted. The discussion shows that the Naïve Bayes model still holds promise for the open problem of unsupervised WSD.…

Taschenbuch. Condición: Neu. The Naïve Bayes Model for Unsupervised Word Sense Disambiguation | Aspects Concerning Feature Selection | Florentina T. Hristea | Taschenbuch | SpringerBriefs in Statistics | xiii | Englisch | 2012 | Springer | EAN 9783642336928 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Condición: new. Questo è un articolo print on demand.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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-based techniques. The benefits of feeding knowledge (of various natures) to a knowledge-lean algorithm for unsupervised WSD that uses the Naïve Bayes model as clustering technique are clearly highlighted. The discussion shows that the Naïve Bayes model still holds promise for the open problem of unsupervised WSD. 84 pp. Englisch.…

Condición: New. Print on Demand pp. 86 4 Illus.

Condición: New. PRINT ON DEMAND pp. 86.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Includes supplementary material: sn.pub/extrasFlorentina T. Hristea is a graduate of the Faculty of Mathematics and Computer Science of the University of Bucharest in 1984. She received her Ph.D. in Mathematics, from the same university, in 1996.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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-based techniques. The benefits of feeding knowledge (of various natures) to a knowledge-lean algorithm for unsupervised WSD that uses the Naïve Bayes model as clustering technique are clearly highlighted. The discussion shows that the Naïve Bayes model still holds promise for the open problem of unsupervised WSD.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 84 pp. Englisch.…