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Python Text Processing with NLTK 2.0 Cookbook
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About Python Text Processing With NLTK 2.0 Cookbook
Use Python's NLTK suite of libraries to maximize your Natural Language Processing capabilities. * Quickly get to grips with Natural Language Processing ? with Text Analysis, Text Mining, and beyond * Learn how machines and crawlers interpret and process natural languages * Easily work with huge amounts of data and learn how to handle distributed processing * Part of Packt's Cookbook series: Each recipe is a carefully organized sequence of instructions to complete the task as efficiently as possible In Detail 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. 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 you dive right into the science of text processing with a practical hands-on approach. 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
