Practical Text Mining with Perl Practical Text Mining with Perl
    • 114,99 €

Publisher Description

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.

GENRE
Computing & Internet
RELEASED
2011
20 September
LANGUAGE
EN
English
LENGTH
320
Pages
PUBLISHER
Wiley
SIZE
15.7
MB

Other Books in This Series

Data Science Using Python and R Data Science Using Python and R
2019
Pattern Recognition Pattern Recognition
2018
Data Mining and Learning Analytics Data Mining and Learning Analytics
2016
Data Mining and Predictive Analytics Data Mining and Predictive Analytics
2015
Discovering Knowledge in Data Discovering Knowledge in Data
2014
Knowledge Discovery with Support Vector Machines Knowledge Discovery with Support Vector Machines
2011