Book Image

Python Text Processing with NLTK 2.0 Cookbook

By : Jacob Perkins
Book Image

Python Text Processing with NLTK 2.0 Cookbook

By: Jacob Perkins

Overview of this book

<p>Natural Language Processing is used everywhere – in search engines, spell checkers, mobile phones, computer games – even your washing machine. Python's Natural Language Toolkit (NLTK) suite of libraries has rapidly emerged as one of the most efficient tools for Natural Language Processing. You want to employ nothing less than the best techniques in Natural Language Processing – and this book is your answer.<br /><br /><em>Python Text Processing with NLTK 2.0 Cookbook</em> is your handy and illustrative guide, which will walk you through all the Natural Language Processing techniques in a step–by-step manner. It will demystify the advanced features of text analysis and text mining using the comprehensive NLTK suite.<br /><br />This book cuts short the preamble and you dive right into the science of text processing with a practical hands-on approach.<br /><br />Get started off with learning tokenization of text. Get an overview of WordNet and how to use it. Learn the basics as well as advanced features of Stemming and Lemmatization. Discover various ways to replace words with simpler and more common (read: more searched) variants. Create your own corpora and learn to create custom corpus readers for JSON files as well as for data stored in MongoDB. Use and manipulate POS taggers. Transform and normalize parsed chunks to produce a canonical form without changing their meaning. Dig into feature extraction and text classification. Learn how to easily handle huge amounts of data without any loss in efficiency or speed.<br /><br />This book will teach you all that and beyond, in a hands-on learn-by-doing manner. Make yourself an expert in using the NLTK for Natural Language Processing with this handy companion.</p>
Table of Contents (16 chapters)
Python Text Processing with NLTK 2.0 Cookbook
Credits
About the Author
About the Reviewers
Preface
Penn Treebank Part-of-Speech Tags
Index

Introduction


NLTK is great for in-memory single-processor natural language processing. However, there are times when you have a lot of data to process and want to take advantage of multiple CPUs, multi-core CPUs, and even multiple computers. Or perhaps you want to store frequencies and probabilities in a persistent, shared database so multiple processes can access it simultaneously. For the first case, we'll be using execnet to do parallel and distributed processing with NLTK. For the second case, you'll learn how to use the Redis data structure server/database to store frequency distributions and more.