000			03451nam a22004695i 4500
001			978-3-642-33693-5
003			DE-He213
005			20140220082855.0
007			cr nn 008mamaa
008			121116s2013 gw | s |||| 0|eng d
020			_a9783642336935 _9978-3-642-33693-5
024	7		_a10.1007/978-3-642-33693-5 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aT. Hristea, Florentina. _eauthor.
245	1	4	_aThe Naïve Bayes Model for Unsupervised Word Sense Disambiguation _h[electronic resource] : _bAspects Concerning Feature Selection / _cby Florentina T. Hristea.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013.
300			_aXIII, 70 p. 4 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Statistics, _x2191-544X
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.
650		0	_aStatistics.
650		0	_aComputer science.
650		0	_aArtificial intelligence.
650	1	4	_aStatistics.
650	2	4	_aStatistics, general.
650	2	4	_aComputer Science, general.
650	2	4	_aArtificial Intelligence (incl. Robotics).
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642336928
830		0	_aSpringerBriefs in Statistics, _x2191-544X
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-33693-5
912			_aZDB-2-SMA
999			_c97408 _d97408