| Survey of Text Mining: Clustering, Classification, and Retrieval 2004 Edition Contributor(s): Berry, Michael W. (Editor) |
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ISBN: 0387955631 ISBN-13: 9780387955636 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: September 2003 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Computers | Intelligence (ai) & Semantics - Computers | Databases - General - Mathematics | Applied |
| Dewey: 006.3 |
| LCCN: 2003042434 |
| Physical Information: 0.69" H x 6.1" W x 9.72" (1.12 lbs) 244 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |