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.
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From the Back Cover
As the volume of digitized textual information continues to grow, so does the critical need for designing robust and 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.
Survey of Text Mining is a comprehensive edited survey organized into three parts: Clustering and Classification; Information Extraction and Retrieval; and Trend Detection. Many of the chapters stress the practical application of software and algorithms for current and future needs in text mining. Authors from industry provide their perspectives on current approaches for large-scale text mining and obstacles that will guide R&D activity in this area for the next decade.
Topics and features:
Highlights issues such as scalability, robustness, and software tools
Brings together recent research and techniques from academia and industry
Examines algorithmic advances in discriminant analysis, spectral clustering, trend detection, and synonym extraction
Includes case studies in mining Web and customer-support logs for hot- topic extraction and query characterizations
Extensive bibliography of all references, including websites
This useful survey volume taps the expertise of academicians and industry professionals to recommend practical approaches to purifying, indexing, and mining textual information. Researchers, practitioners, and professionals involved in information retrieval, computational statistics, and data mining, who need the latest text-mining methods and algorithms, will find the book an indispensable resource.
About the Author
Michael Berry is associate professor of contemporary Chinese cultural studies at the University of California, Santa Barbara.
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.
**
From the Back Cover
As the volume of digitized textual information continues to grow, so does the critical need for designing robust and 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.
Survey of Text Mining is a comprehensive edited survey organized into three parts: Clustering and Classification; Information Extraction and Retrieval; and Trend Detection. Many of the chapters stress the practical application of software and algorithms for current and future needs in text mining. Authors from industry provide their perspectives on current approaches for large-scale text mining and obstacles that will guide R&D activity in this area for the next decade.
Topics and features:
This useful survey volume taps the expertise of academicians and industry professionals to recommend practical approaches to purifying, indexing, and mining textual information. Researchers, practitioners, and professionals involved in information retrieval, computational statistics, and data mining, who need the latest text-mining methods and algorithms, will find the book an indispensable resource.
About the Author
Michael Berry is associate professor of contemporary Chinese cultural studies at the University of California, Santa Barbara.