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Spotting and Discovering Terms Through Natural Language Processing

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Synopses & Reviews

Publisher Comments:

andlt;Pandgt;In this book Christian Jacquemin shows how the power of natural language processing (NLP) can be used to advance text indexing and information retrieval (IR). Jacquemin's novel tool is FASTR, a parser that normalizes terms and recognizes term variants. Since there are more meanings in a language than there are words, FASTR uses a metagrammar composed of shallow linguistic transformations that describe the morphological, syntactic, semantic, and pragmatic variations of words and terms. The acquired parsed terms can then be applied for precise retrieval and assembly of information.The use of a corpus-based unification grammar to define, recognize, and combine term variants from their base forms allows for intelligent information access to, or "linguistic data tuning" of, heterogeneous texts. FASTR can be used to do automatic controlled indexing, to carry out content-based Web searches through conceptually related alternative query formulations, to abstract scientific and technical extracts, and even to translate and collect terms from multilingual material. Jacquemin provides a comprehensive account of the method and implementation of this innovative retrieval technique for text processing.andlt;/Pandgt;

Synopsis:

Christian Jacquemin shows how the power of natural language processing (NLP) can be used to advance text indexing and information retrieval (IR).

Synopsis:

andlt;Pandgt;Christian Jacquemin shows how the power of natural language processing (NLP) can be used to advance text indexing and information retrieval (IR).andlt;/Pandgt;

Synopsis:

In this book Christian Jacquemin shows how the power of natural language processing (NLP) can be used to advance text indexing and information retrieval (IR). Jacquemin's novel tool is FASTR, a parser that normalizes terms and recognizes term variants. Since there are more meanings in a language than there are words, FASTR uses a metagrammar composed of shallow linguistic transformations that describe the morphological, syntactic, semantic, and pragmatic variations of words and terms. The acquired parsed terms can then be applied for precise retrieval and assembly of information.

Synopsis:

In this book Christian Jacquemin shows how the power of natural language processing (NLP) can be used to advance text indexing and information retrieval (IR). Jacquemin's novel tool is FASTR, a parser that normalizes terms and recognizes term variants. Since there are more meanings in a language than there are words, FASTR uses a metagrammar composed of shallow linguistic transformations that describe the morphological, syntactic, semantic, and pragmatic variations of words and terms. The acquired parsed terms can then be applied for precise retrieval and assembly of information.The use of a corpus-based unification grammar to define, recognize, and combine term variants from their base forms allows for intelligent information access to, or "linguistic data tuning" of, heterogeneous texts. FASTR can be used to do automatic controlled indexing, to carry out content-based Web searches through conceptually related alternative query formulations, to abstract scientific and technical extracts, and even to translate and collect terms from multilingual material. Jacquemin provides a comprehensive account of the method and implementation of this innovative retrieval technique for text processing.

About the Author

Christian Jacquemin is Professor at the University of Paris 11 and Researcher in Computer Science at CNRS-LIMSI (Centre National de la Recherche Scientifique, Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur).

Product Details

ISBN:
9780262100854
Author:
Jacquemin, Christian
Publisher:
The MIT Press
Author:
tian Jacquemin
Author:
Chris
Location:
Cambridge, Mass.
Subject:
Computer Science
Subject:
Programming Languages - General
Subject:
Terms and phrases
Subject:
Language and languages
Subject:
Natural Language Processing
Subject:
Terms and phrases -- Data processing.
Subject:
Language and languages -- Variation.
Subject:
Software Engineering - Programming and Languages
Edition Description:
Includes bibliographical references and index.
Series:
Spotting and Discovering Terms through Natural Language Processing
Series Volume:
105-101
Publication Date:
20010427
Binding:
Hardback
Grade Level:
from 17
Language:
English
Illustrations:
71 illus.
Pages:
357
Dimensions:
9 x 7 in 1.8 lb

Related Subjects

Computers and Internet » Artificial Intelligence » Natural Language
Computers and Internet » Computers Reference » General
Computers and Internet » Personal Computers » General
Computers and Internet » Software Engineering » Programming and Languages

Spotting and Discovering Terms Through Natural Language Processing New Hardcover
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Product details 357 pages MIT Press - English 9780262100854 Reviews:
"Synopsis" by , Christian Jacquemin shows how the power of natural language processing (NLP) can be used to advance text indexing and information retrieval (IR).
"Synopsis" by , andlt;Pandgt;Christian Jacquemin shows how the power of natural language processing (NLP) can be used to advance text indexing and information retrieval (IR).andlt;/Pandgt;
"Synopsis" by , In this book Christian Jacquemin shows how the power of natural language processing (NLP) can be used to advance text indexing and information retrieval (IR). Jacquemin's novel tool is FASTR, a parser that normalizes terms and recognizes term variants. Since there are more meanings in a language than there are words, FASTR uses a metagrammar composed of shallow linguistic transformations that describe the morphological, syntactic, semantic, and pragmatic variations of words and terms. The acquired parsed terms can then be applied for precise retrieval and assembly of information.
"Synopsis" by , In this book Christian Jacquemin shows how the power of natural language processing (NLP) can be used to advance text indexing and information retrieval (IR). Jacquemin's novel tool is FASTR, a parser that normalizes terms and recognizes term variants. Since there are more meanings in a language than there are words, FASTR uses a metagrammar composed of shallow linguistic transformations that describe the morphological, syntactic, semantic, and pragmatic variations of words and terms. The acquired parsed terms can then be applied for precise retrieval and assembly of information.The use of a corpus-based unification grammar to define, recognize, and combine term variants from their base forms allows for intelligent information access to, or "linguistic data tuning" of, heterogeneous texts. FASTR can be used to do automatic controlled indexing, to carry out content-based Web searches through conceptually related alternative query formulations, to abstract scientific and technical extracts, and even to translate and collect terms from multilingual material. Jacquemin provides a comprehensive account of the method and implementation of this innovative retrieval technique for text processing.
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