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عنوان
Latent semantic mapping :

پدید آورنده
Jerome R. Bellegarda.

موضوع
Automatic speech recognition.,Computational linguistics.,Latent semantic indexing.,Semantics-- Data processing.,Semantics-- Mathematical models.,Automatic speech recognition.,Computational linguistics.,LANGUAGE ARTS & DISCIPLINES-- Linguistics-- Psycholinguistics.,Latent semantic indexing.,Semantics-- Data processing.,Semantics-- Mathematical models.

رده
P325
.
5
.
D38
B45
2007

کتابخانه
Center and Library of Islamic Studies in European Languages

محل استقرار
استان: Qom ـ شهر: Qom

Center and Library of Islamic Studies in European Languages

تماس با کتابخانه : 32910706-025

INTERNATIONAL STANDARD BOOK NUMBER

(Number (ISBN
159829105X
(Number (ISBN
1598294032
(Number (ISBN
9781598291056
(Number (ISBN
9781598294033
Erroneous ISBN
1598291041
Erroneous ISBN
9781598291049

NATIONAL BIBLIOGRAPHY NUMBER

Number
b800931

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Latent semantic mapping :
General Material Designation
[Book]
Other Title Information
principles & applications /
First Statement of Responsibility
Jerome R. Bellegarda.

EDITION STATEMENT

Edition Statement
1st ed.

.PUBLICATION, DISTRIBUTION, ETC

Place of Publication, Distribution, etc.
[San Rafael, Calif.] :
Name of Publisher, Distributor, etc.
Morgan & Claypool Publishers,
Date of Publication, Distribution, etc.
©2007.

PHYSICAL DESCRIPTION

Specific Material Designation and Extent of Item
1 online resource (x, 101 pages)

SERIES

Series Title
Synthesis lectures on speech and audio processing ;
Volume Designation
#3

GENERAL NOTES

Text of Note
Title from PDF title page (viewed Sept. 13, 2007).

INTERNAL BIBLIOGRAPHIES/INDEXES NOTE

Text of Note
Includes bibliographical references (pages 89-100).

CONTENTS NOTE

Text of Note
Principles -- Introduction -- Motivation -- From LSA to LSM -- Organization -- Latent semantic mapping -- Co-occurrence matrix -- Vector representation -- Interpretation -- LSM feature space -- Closeness measures -- LSM framework extension -- Salient characteristics -- Computational effort -- Off-line cost -- Online cost -- Possible shortcuts -- Probabilistic extensions -- Dual probability model -- Probabilistic latent semantic analysis -- Inherent limitations -- Applications -- Junk e-mail filtering -- Conventional approaches -- LSM-based filtering -- Performance -- Semantic classification -- Underlying issues -- Semantic inference -- Caveats -- Language modeling -- N-gram limitations -- MultiSpan language modeling -- Smoothing -- Pronunciation modeling -- Grapheme-to-phoneme conversion -- Pronunciation by latent analogy -- Speaker verification -- The task -- LSM-based speaker verification -- TTS unit selection -- Concatenative synthesis -- LSM-based unit selection -- LSM-based boundary training -- Perspectives -- Discussion -- Inherent tradeoffs -- General applicability -- Conclusion -- Summary -- Perspectives.
0

SUMMARY OR ABSTRACT

Text of Note
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. This approach exhibits three main characteristics: 1) discrete entities (words and documents) are mapped onto a continuous vector space; 2) this mapping is determined by global correlation patterns; and 3) 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.

OTHER EDITION IN ANOTHER MEDIUM

International Standard Book Number
9781598291049

TOPICAL NAME USED AS SUBJECT

Automatic speech recognition.
Computational linguistics.
Latent semantic indexing.
Semantics-- Data processing.
Semantics-- Mathematical models.
Automatic speech recognition.
Computational linguistics.
LANGUAGE ARTS & DISCIPLINES-- Linguistics-- Psycholinguistics.
Latent semantic indexing.
Semantics-- Data processing.
Semantics-- Mathematical models.

(SUBJECT CATEGORY (Provisional

LAN-- 009040

DEWEY DECIMAL CLASSIFICATION

Number
401/
.
9
Edition
22

LIBRARY OF CONGRESS CLASSIFICATION

Class number
P325
.
5
.
D38
Book number
B45
2007

PERSONAL NAME - PRIMARY RESPONSIBILITY

Bellegarda, Jerome Rene,1961-

ORIGINATING SOURCE

Date of Transaction
20201207131737.0
Cataloguing Rules (Descriptive Conventions))
pn

ELECTRONIC LOCATION AND ACCESS

Electronic name
 مطالعه متن کتاب 

[Book]

Y

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