Practical Text Mining with Perl (Hardcover)

Roger Bilisoly

ISBN 10: 0470176431 / ISBN 13: 9780470176436

Editore: John Wiley & Sons Inc, New York, 2008

Riguardo questo articolo

Hardcover. Provides readers with the methods, algorithms, and means to perform text mining tasks This book is devoted to the fundamentals of text mining using Perl, an open-source programming tool that is freely available via the Internet . It covers mining ideas from several perspectives--statistics, data mining, linguistics, and information retrieval--and provides readers with the means to successfully complete text mining tasks on their own. The book begins with an introduction to regular expressions, a text pattern methodology, and quantitative text summaries, all of which are fundamental tools of analyzing text. Then, it builds upon this foundation to explore: Probability and texts, including the bag-of-words modelInformation retrieval techniques such as the TF-IDF similarity measureConcordance lines and corpus linguisticsMultivariate techniques such as correlation, principal components analysis, and clusteringPerl modules, German, and permutation tests Each chapter is devoted to a single key topic, and the author carefully and thoughtfully introduces mathematical concepts as they arise, allowing readers to learn as they go without having to refer to additional books. The inclusion of numerous exercises and worked-out examples further complements the book's student-friendly format. Practical Text Mining with Perl is ideal as a textbook for undergraduate and graduate courses in text mining and as a reference for a variety of professionals who are interested in extracting information from text documents. Provides readers with the methods, algorithms, and means to perform text mining tasks This book is devoted to the fundamentals of text mining using Perl, an open-source programming tool that is freely available via the Internet (www. perl. org). Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Codice articolo 9780470176436

Dati bibliografici

• Titolo: Practical Text Mining with Perl (Hardcover)
• Editore: John Wiley & Sons Inc, New York
• Data di pubblicazione: 2008
• Lingua: Inglese
• ISBN 10: 0470176431
• ISBN 13: 9780470176436
• Rilegatura: Hardcover
• Edizione: prima edizione
• Condizione: new

Descrizione articolo

Riassunto

Provides readers with the methods, algorithms, and means to perform text mining tasks

This book is devoted to the fundamentals of text mining using Perl, an open-source programming tool that is freely available via the Internet (www.perl.org). It covers mining ideas from several perspectives--statistics, data mining, linguistics, and information retrieval--and provides readers with the means to successfully complete text mining tasks on their own.

The book begins with an introduction to regular expressions, a text pattern methodology, and quantitative text summaries, all of which are fundamental tools of analyzing text. Then, it builds upon this foundation to explore:

• Probability and texts, including the bag-of-words model
• Information retrieval techniques such as the TF-IDF similarity measure
• Concordance lines and corpus linguistics
• Multivariate techniques such as correlation, principal components analysis, and clustering
• Perl modules, German, and permutation tests

Each chapter is devoted to a single key topic, and the author carefully and thoughtfully introduces mathematical concepts as they arise, allowing readers to learn as they go without having to refer to additional books. The inclusion of numerous exercises and worked-out examples further complements the book's student-friendly format.

Practical Text Mining with Perl is ideal as a textbook for undergraduate and graduate courses in text mining and as a reference for a variety of professionals who are interested in extracting information from text documents.

Informazioni sull?autore

Roger Bilisoly, PhD, is an Assistant Professor of Statistics at Central Connecticut State University, where he developed and teaches a new graduate-level course in text mining for the school's data mining program.