Perkins J. - Python Text Processing with NLTK 2.0 Cookbook [2010, PDF, ENG] + Code

Python Text Processing with NLTK 2.0 Cookbook

Год: 2010
Автор: Jacob Perkins
Издательство: Packt Publishing
ISBN: 1849513600, 978-1849513609
Язык: Английский
Формат: PDF
Качество: Изначально компьютерное (eBook)
Количество страниц: 272

Описание: Use Python's NTLK 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 (NTLK) 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 NTLK 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 NTLK 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 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 NTLK for Natural Language Processing with this handy companion.

What you will learn from this book
Learn Text categorization and Topic identification
Learn Stemming and Lemmatization and how to go beyond the usual spell checker
Replace negations with antonyms in your text
Learn to tokenize words into lists of sentences and words, and gain an insight into WordNet
Transform and manipulate chunks and trees
Learn advanced features of corpus readers and create your own custom corpora
Tag different parts of speech by creating, training, and using a part-of-speech tagger
Improve accuracy by combining multiple part-of-speech taggers
Learn how to do partial parsing to extract small chunks of text from a part-of-speech tagged sentence
Produce an alternative canonical form without changing the meaning by normalizing parsed chunks
Learn how search engines use Natural Language Processing to process text
Make your site more discoverable by learning how to automatically replace words with more searched equivalents
Parse dates, times, and HTML
Train and manipulate different types of classifiers

Approach

The learn-by-doing approach of this book will enable you to dive right into the heart of text processing from the very first page. Each recipe is carefully designed to fulfill your appetite for Natural Language Processing. Packed with numerous illustrative examples and code samples, it will make the task of using the NTLK for Natural Language Processing easy and straightforward.

Who this book is written for

This book is for Python programmers who want to quickly get to grips with using the NLTK for Natural Language Processing. Familiarity with basic text processing concepts is required. Programmers experienced in the NTLK will also find it useful. Students of linguistics will find it invaluable.
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