Introduction to Information Retrieval
The book aims to provide a modern approach to information retrieval from a computer science perspective. It is based on a course Christopher Manning, Prabhakar Raghavan and Hinrich Schütze have
been teaching in various forms at Stanford University and at the University of Stuttgart.
Mining the Talk: Unlocking the Business Value in Unstructured Information
In Mining the Talk, two leading-edge IBM researchers – Scott Spangler and Jeffrey Kreulen – introduce a revolutionary new approach to unlocking the business value hidden in
virtually any form of unstructured data – from word processing documents to websites, e-mails to instant messages.
Survey of Text Mining II: Clustering, Classification, and Retrieval
The development of techniques for mining unstructured, semi-structured, and fully-structured textual data has become increasingly important in both academia and industry. This second volume by
Michael Berry and Malu Castellanos continues to survey the evolving field of text mining - the application of techniques of machine learning, in conjunction with natural language processing,
information extraction and algebraic/mathematical approaches, to computational information retrieval. Numerous diverse issues are addressed, ranging from the development of new learning approaches to
novel document clustering algorithms, collectively spanning several major topic areas in text mining.
Text Mining: Predictive Methods for Analyzing Unstructured Information
This book by Sholom Weiss, Nitin Indurkhya, Tong Zhang and Fred Damerau focuses on the concepts and methods needed to expand horizons beyond structured, numeric data to automated mining of text
samples. It introduces the new world of text mining and examines proven methods for various critical text-mining tasks, such as automated document indexing and information retrieval and search. New
research areas are explored, such as information extraction and document summarization, that rely on evolving text-mining techniques.
The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text mining tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management.
In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, this book by Ronen Feldman and James Sanger examines advanced pre-processing
techniques, knowledge representation considerations, and visualization approaches. Finally, it explores current real-world, mission-critical applications of text mining and link detection in such
varied fields as M&A business intelligence, genomics research and counter-terrorism activities.
