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Latent semantic mapping : principles & applications

Author: Jerome Rene Bellegarda
Publisher: [San Rafael, Calif.] : Morgan & Claypool, ©2007.
Series: Synthesis lectures on speech and audio processing, #3.
Edition/Format:   Print book : English : 1st edView all editions and formats
Summary:
"Latent 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,  Read more...
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Jerome Rene Bellegarda
ISBN: 9781598291049 1598291041 159829105X 9781598291056
OCLC Number: 144520876
Description: x, 101 pages : illustrations ; 24 cm.
Contents: I. PrinciplesIntroductionLatent Semantic MappingLSM Feature SpaceComputational EffortProbabilistic ExtensionsII. ApplicationsJunk E-mail FilteringSemantic ClassificationLanguage ModelingPronunciation ModelingSpeaker VerificationTTS Unit SelectionIII. PerspectivesDiscussionConclusionBibliography
Series Title: Synthesis lectures on speech and audio processing, #3.
Responsibility: Jerome R. Bellegarda.

Abstract:

Offers a general overview of a data-driven framework for modelling meaningful global relationships implicit in large volumes of (not necessarily textual) data, and underscores the multifaceted  Read more...

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   schema:reviewBody ""Latent 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." "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."--Jacket." ;
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