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Table of contents (9 chapters)
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Front Matter
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Information Extraction and Retrieval
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Front Matter
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Trend Detection
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Front Matter
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Back Matter
Keywords
About this book
Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory.
As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments.
This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.
Editors and Affiliations
Bibliographic Information
Book Title: Survey of Text Mining
Book Subtitle: Clustering, Classification, and Retrieval
Editors: Michael W. Berry
DOI: https://doi.org/10.1007/978-1-4757-4305-0
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2004
Hardcover ISBN: 978-0-387-95563-6Published: 09 September 2003
Softcover ISBN: 978-1-4419-3057-6Published: 09 October 2011
eBook ISBN: 978-1-4757-4305-0Published: 14 March 2013
Edition Number: 1
Number of Pages: XVII, 244
Number of Illustrations: 46 b/w illustrations
Topics: Multimedia Information Systems, Information Storage and Retrieval, Information Systems and Communication Service, Applications of Mathematics