Python Text Processing with NLTK 2.0 Cookbook.pdf

Preface Natural Language Processing is used everywhere—in search engines, spell checkers, mobile phones, computer games, and even in 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. Python Text Processing with NLTK 2.0 Cookbook 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. This book cuts short the preamble and lets you dive right into the science of text processing with a practical hands-on approach. Get started off with learning tokenization of text. Receive 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 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. 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. Preface 2 What this book covers Chapter 1, Tokenizing Text and WordNet Basics, covers the basics of tokenizing text and using WordNet. Chapter 2, Replacing and Correcting Words, discusses various word replacement and correction techniques. The recipes cover the gamut of linguistic compression, spelling co