Practical Text Mining with Perl

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).

Informationen zum Autor 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. Klappentext Provides readers with the methods, algorithms, and means to perform text mining tasksThis 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 testsEach 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. Zusammenfassung Provides readers with the methods, algorithms, and means to perform text mining tasksThis 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 testsEach 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. Inhaltsverzeichnis List of Figures.List of Tables.Preface.Acknowledgments.1. Introduction.2. Text Patterns.3. Quantitative Text Summaries.4. Probability and Text Sampling.5. Applying Information Retrieval to Text Mining.6. Concordance Lines and Corpus Linguistics.7. Multivariate Techniques with Text.8. Text Clustering.9. A Sample of Additional Topics.Appendix A. Overview of Perl for Text Mining.Appendix B. Summary of R used in this Book.References.Index....

Autorentext
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.

Inhalt
List of Figures. List of Tables. Preface. Acknowledgments. 1. Introduction. 2. Text Patterns. 3. Quantitative Text Summaries. 4. Probability and Text Sampling. 5. Applying Information Retrieval to Text Mining. 6. Concordance Lines and Corpus Linguistics. 7. Multivariate Techniques with Text. 8. Text Clustering. 9. A Sample of Additional Topics. Appendix A. Overview of Perl for Text Mining. Appendix B. Summary of R used in this Book. References. Index.

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