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020 _a9783642336935
_99783642336935
024 7 _a10.1007/9783642336935
_2doi
035 _avtls000360310
039 9 _a201509030603
_bVLOAD
_c201405070257
_dVLOAD
_y201402201429
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA276-280
100 1 _aT. Hristea, Florentina.
_eautor
_9346088
245 1 4 _aThe Naïve Bayes Model for Unsupervised Word Sense Disambiguation :
_bAspects Concerning Feature Selection /
_cby Florentina T. Hristea.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _axiii, 70 páginas 4 ilustraciones
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aSpringerBriefs in Statistics,
_x2191-544X
500 _aSpringer eBooks
505 0 _a1.Preliminaries -- 2.The Naïve Bayes Model in the Context of Word Sense Disambiguation -- 3.Semantic WordNet-based Feature Selection -- 4.Syntactic Dependency-based Feature Selection -- 5.N-Gram Features for Unsupervised WSD with an Underlying Naïve Bayes Model References -- Index.  .
520 _aThis 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.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9783642336928
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-642-33693-5
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c306726
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