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003 DE-He213
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020 _a9783031025563
_9978-3-031-02556-3
024 7 _a10.1007/978-3-031-02556-3
_2doi
050 4 _aTK1-9971
072 7 _aTHR
_2bicssc
072 7 _aTEC007000
_2bisacsh
072 7 _aTHR
_2thema
082 0 4 _a621.3
_223
100 1 _aBellegarda, Jerome R.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985859
245 1 0 _aLatent Semantic Mapping
_h[electronic resource] :
_bPrinciples and Applications /
_cby Jerome R. Bellegarda.
250 _a1st ed. 2007.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2007.
300 _aX, 101 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aElectrical engineering.
_985861
650 0 _aSignal processing.
_94052
650 0 _aAcoustical engineering.
_99499
650 1 4 _aElectrical and Electronic Engineering.
_985862
650 2 4 _aSignal, Speech and Image Processing.
_931566
650 2 4 _aEngineering Acoustics.
_931982
710 2 _aSpringerLink (Online service)
_985865
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031014284
776 0 8 _iPrinted edition:
_z9783031036842
830 0 _aSynthesis Lectures on Speech and Audio Processing,
_x1932-1678
_985867
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02556-3
912 _aZDB-2-SXSC
942 _cEBK
999 _c85869
_d85869