Practical Text Mining with Perl (Hardcover)

Roger Bilisoly

ISBN 10: 0470176431 / ISBN 13: 9780470176436

Edité par John Wiley & Sons Inc, New York, 2008

A propos de cet article

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 UK warehouse or from our Australian or US warehouses, depending on stock availability.

N° de réf. du vendeur 9780470176436

Détails bibliographiques

• Titre: Practical Text Mining with Perl (Hardcover)
• Éditeur: John Wiley & Sons Inc, New York
• Date de parution: 2008
• Langue: anglais
• ISBN à 10 chiffres: 0470176431
• ISBN à 13 chiffres: 9780470176436
• Reliure: Hardcover
• Édition: Edition originale
• État de l'article: new

A propos de ce titre

Synopsis

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.

À propos de l?auteur

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.