Text Mining Application Programming / Edition 1

Text Mining Application Programming / Edition 1

5.0 2
by Manu Konchady
     
 

Text mining offers a way for individuals and corporations to exploit the vast amount of information available on the Internet. Text Mining Application Programming teaches developers about the problems of managing unstructured text, and describes how to build tools for text mining using standard statistical methods from Artificial Intelligence and Operations

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Overview

Text mining offers a way for individuals and corporations to exploit the vast amount of information available on the Internet. Text Mining Application Programming teaches developers about the problems of managing unstructured text, and describes how to build tools for text mining using standard statistical methods from Artificial Intelligence and Operations Research. These tools can be used for a variety of fields, including law, business, and medicine. Key topics covered include, information extraction, clustering, text categorization, searching the Web, summarization, and natural language query systems. The book explains the theory behind each topic and algorithm, and then provides a practical solution implementation with which developers and students can experiment. A wide variety of code is also included for developers to build their own custom solutions. After reading through this book developers will be able to tap into the bevy information available online in ways they never thought possible and students will have a thorough understanding of the theory and practical application of text mining.

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Product Details

ISBN-13:
9781584504603
Publisher:
Cengage Learning
Publication date:
05/04/2006
Series:
Charles River Media Programming Series
Edition description:
BK&CD-ROM
Pages:
412
Sales rank:
1,360,978
Product dimensions:
9.02(w) x 7.38(h) x 1.03(d)
Age Range:
3 Months

Related Subjects

Table of Contents

Preface Acknowledgments Chapter 1 Introduction Chapter 2 Mathematics Background Chapter 3 Exploring Text Chapter 4 Markov Models and POS Tagging Chapter 5 Information Extraction Chapter 6 Search Engines Chapter 7 Searching the Web Chapter 8 Clustering Documents Chapter 9 Text Categorization Chapter 10 Summarization Chapter 11 Question & Answer About the CD-ROM Index

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