Fundamentals of Predictive Text Mining (Texts in Computer Science)

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9781849962254: Fundamentals of Predictive Text Mining (Texts in Computer Science)

From the reviews: "This is a practical, up-to-date account of the various techniques for dealing intelligently with free text. It would be an invaluable resource to any advanced undergraduate student interested in information retrieval." (Patrick Oladimeji, Times Higher Education, 26 May 2011) "This is a well-written and interesting text for information technology (IT) professionals and computer science students. It seems to address all of the topics related to the fields that, when integrated, are known as knowledge engineering. ... Without a doubt, the authors' experience in the field makes this book a successful contribution to the literature that targets the interests of the IT community and beyond." (Jolanta Mizera-Pietraszko, ACM Computing Reviews, June, 2011) "This well-written work, which offers a unifying view of text mining through a systematic introduction to solving real-world problems. ... The uniqueness of this book is the recourse to the prediction problem, which, by providing practical advice, allows for the integration of related topics. ... The book is accompanied by a software implementation of the main algorithmic practices introduced. This is the icing on the cake for both beginners and expert readers ... . This is the book ... I have always wanted to read." (Ernesto D'Avenzo, ACM Computing Reviews, August, 2012)

Vom Verlag:

One consequence of the pervasive use of computers is that most documents originate in digital form. Widespread use of the Internet makes them readily available. Text mining - the process of analyzing unstructured natural-language text - is concerned with how to extract information from these documents. Developed from the authors' highly successful Springer reference on text mining, Fundamentals of Predictive Text Mining is an introductory textbook and guide to this rapidly evolving field. Integrating topics spanning the varied disciplines of data mining, machine learning, databases, and computational linguistics, this uniquely useful book also provides practical advice for text mining. In-depth discussions are presented on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Background on data mining is beneficial, but not essential. Where advanced concepts are discussed that require mathematical maturity for a proper understanding, intuitive explanations are also provided for less advanced readers. Topics and features: presents a comprehensive, practical and easy-to-read introduction to text mining; includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter; explores the application and utility of each method, as well as the optimum techniques for specific scenarios; provides several descriptive case studies that take readers from problem description to systems deployment in the real world; includes access to industrial-strength text-mining software that runs on any computer; describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English); contains links to free downloadable software and other supplementary instruction material. Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students. Dr. Sholom M. Weiss is a Research Staff Member with the IBM Predictive Modeling group, in Yorktown Heights, New York, and Professor Emeritus of Computer Science at Rutgers University. Dr. Nitin Indurkhya is Professor at the School of Computer Science and Engineering, University of New South Wales, Australia, as well as founder and president of data-mining consulting company Data-Miner Pty Ltd. Dr. Tong Zhang is Associate Professor at the Department of Statistics and Biostatistics at Rutgers University, New Jersey.

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