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003			ES-MaUEC
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007			cr nn 008mamaa
008			220601s2007 sz | s |||| 0|eng d
020			_a9783031025563
024	7		_a10.1007/978-3-031-02556-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aP325.5.D38 _b2007 EB
100	1		_aBellegarda, Jerome Rene, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687656 _d1961-
245	1	0	_aLatent Semantic Mapping : _bPrinciples and Applications _cby Jerome R. Bellegarda
250			_a1st edition 2007
264		1	_aCham _bSpringer International Publishing _c2007
300			_a1 recurso en línea (X, 101 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Speech and Audio Processing _x1932-1678
505	0		_aContents: I. Principles -- Introduction -- Latent Semantic Mapping -- LSM Feature Space -- Computational Effort -- Probabilistic Extensions -- II. Applications -- Junk E-mail Filtering -- Semantic Classification -- Language Modeling -- Pronunciation Modeling -- Speaker Verification -- TTS Unit Selection -- III. Perspectives -- Discussion -- Conclusion -- Bibliography.
520			_aLatent semantic mapping (LSM) is a generalization of latent semantic analysis (LSA), a paradigm originally developed to capture hidden word patterns in a text document corpus. In information retrieval, LSA enables retrieval on the basis of conceptual content, instead of merely matching words between queries and documents. It operates under the assumption that there is some latent semantic structure in the data, which is partially obscured by the randomness of word choice with respect to retrieval. Algebraic and/or statistical techniques are brought to bear to estimate this structure and get rid of the obscuring ""noise."" This results in a parsimonious continuous parameter description of words and documents, which then replaces the original parameterization in indexing and retrieval. This approach exhibits three main characteristics: -Discrete entities (words and documents) are mapped onto a continuous vector space; -This mapping is determined by global correlation patterns; and -Dimensionality reduction is an integral part of the process. Such fairly generic properties are advantageous in a variety of different contexts, which motivates a broader interpretation of the underlying paradigm. The outcome (LSM) is a data-driven framework for modeling meaningful global relationships implicit in large volumes of (not necessarily textual) data. This monograph gives a general overview of the framework, and underscores the multifaceted benefits it can bring to a number of problems in natural language understanding and spoken language processing. It concludes with a discussion of the inherent tradeoffs associated with the approach, and some perspectives on its general applicability to data-driven information extraction. Contents: I. Principles / Introduction / Latent Semantic Mapping / LSM Feature Space / Computational Effort / Probabilistic Extensions / II. Applications / Junk E-mail Filtering / Semantic Classification / Language Modeling / Pronunciation Modeling / Speaker Verification / TTS Unit Selection / III. Perspectives / Discussion / Conclusion / Bibliography.
988			_aSynthesis Collection of Technology_2007
650		7	_2embne _9140153 _aSemántica _xModelos matematicos
650		7	_2embne _9147323 _aReconocimiento automático del lenguaje
650		7	_2embne _9666075 _aLingüística computacional
776	0	8	_iPrinted edition: _z9783031014284
776	0	8	_iPrinted edition: _z9783031036842
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02556-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI