Bilisoly Practical Text Mining with Perl

1. Auflage 2008
ISBN: 978-0-470-38285-1
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 320 Seiten, E-Book

Reihe: Wiley Series on Methods and Applications

Provides readers with the methods, algorithms, and means toperform text mining tasks
This book is devoted to the fundamentals of text mining usingPerl, an open-source programming tool that is freely available viathe Internet (www.perl.org). It covers mining ideas from severalperspectives--statistics, data mining, linguistics, and informationretrieval--and provides readers with the means to successfullycomplete text mining tasks on their own.
The book begins with an introduction to regular expressions, atext pattern methodology, and quantitative text summaries, all ofwhich are fundamental tools of analyzing text. Then, it builds uponthis foundation to explore:
* Probability and texts, including the bag-of-words model
* Information retrieval techniques such as the TF-IDF similaritymeasure
* Concordance lines and corpus linguistics
* Multivariate techniques such as correlation, principalcomponents analysis, and clustering
* Perl modules, German, and permutation tests
Each chapter is devoted to a single key topic, and the authorcarefully and thoughtfully introduces mathematical concepts as theyarise, allowing readers to learn as they go without having to referto additional books. The inclusion of numerous exercises andworked-out examples further complements the book's student-friendlyformat.
Practical Text Mining with Perl is ideal as a textbookfor undergraduate and graduate courses in text mining and as areference for a variety of professionals who are interested inextracting information from text documents.

Autoren/Hrsg.

Bilisoly, Roger

Inhaltsverzeichnis

List of Figures.
List of Tables.
Preface.
Acknowledgments.
1. Introduction.
1.1 Overview of this Book.
1.2 Text Mining and Related Fields.
1.3 Advice for Reading this Book.
2. Text Patterns.
2.1 Introduction.
2.2 Regular Expressions.
2.3 Finding Words in a Text.
2.4 Decomposing Poe's "The Tell-Tale Heart" into Words.
2.5 A Simple Concordance.
2.6 First Attempt at Extracting Sentences.
2.7 Regex Odds and Ends.
2.8 References.
3. Quantitative Text Summaries.
3.1 Introduction.
3.2 Scalars, Interpolation, and Context in Perl.
3.3 Arrays and Context in Perl.
3.4 Word Lengths in Poe's "The Tell-Tale Heart".
3.5 Arrays and Functions.
3.6 Hashes.
3.7 Two Text Applications.
3.8 Complex Data Structures.
3.9 References.
3.10 First Transition.
4. Probability and Text Sampling.
4.1 Introduction.
4.2 Probability.
4.3 Conditioned Probability.
4.4 Mean and Variance of random Variables.
4.5 The Bag-of-Words Model for Poe's :The Black Cat".
4.6 The Effect of Sample Size.
4.7 References.
5. Applying Information Retrieval to Text Mining.
5.1 Introduction.
5.2 Counting Letters and Words.
5.3 Text Counts and Vectors.
5.4 The Term-Document Matrix Applied to Poe.
5.5 Matrix Multiplication.
5.6 Functions of Counts.
5.7 Document Similarity.
5.8 References.
6. Concordance Lines and Corpus Linguistics.
6.1 Introduction.
6.2 Sampling.
6.3 Corpus as Baseline.
6.4 Concordancing.
6.5 Collocations and Concordance Lines.
6.6 Applications with References.
6.7 Second Transition.
7. Multivariate Techniques with Text.
7.1 Introduction.
7.2 Basic Statistics.
7.3 Basic Linear Algebra.
7.4 Principal Component Matrices.
7.5 Text Applications.
7.6 Applications and References.
8. Text Clustering.
8.1 Introduction.
8.2 Clustering.
8.3 A Note on Classification.
8.4 References.
8.5 Last Transition.
9. A Sample of Additional Topics.
9.1 Introduction.
9.2 Perl Modules.
9.3 Other Languages: Analyzing Goethe in German.
9.4 Permutation Tests.
9.5 References.
Appendix A. Overview of Perl for Text Mining.
A.1 Basic Data Structures.
A.2 Operators.
A.3 Branching and Looping.
A.4 A Few Functions.
A.5 Introduction to Regular Expressions.
Appendix B. Summary of R used in this Book
B.1 Basics of R.
B.2 This Book's R Code..
References.
Index.

Über Autor(innen)

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.