Python Text Processing with NLTK 2.0 Cookbook: LITE


Python Text Processing with NLTK 2.0 Cookbook: LITE
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Overview
Table of Contents
Author
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Sample Chapters

 

 

  • Quickly get to grips with Natural Language Processing – with Tokenizing Text, WordNet, and beyond
  • Learn how to replace and correct words to improve search engine results
  • Categorize documents and pieces of text and assign class labels
  • Part of Packt's Cookbook series: Each recipe is a carefully organized sequence of instructions to complete the task as efficiently as possible

 

 

Book Details

Language : English
Paperback : 92 pages [ mm x mm ]
Release Date : May 2011
ISBN : 1849516383
ISBN 13 : 9781849516389
Author(s) : Jacob Perkins
Topics and Technologies : All Books, Application Development, Lite Editions

Table of Contents

Preface
Chapter 1: Tokenizing Text and WordNet Basics
Chapter 2: Replacing and Correcting Words
Chapter 3: Text Classification
Index
  • Chapter 1: Tokenizing Text and WordNet Basics
    • Introduction
    • Tokenizing text into sentences
    • Tokenizing sentences into words
    • Tokenizing sentences using regular expressions
    • Filtering stopwords in a tokenized sentence
    • Looking up synsets for a word in WordNet
    • Looking up lemmas and synonyms in WordNet
    • Calculating WordNet synset similarity
    • Discovering word collocations
    • Chapter 2: Replacing and Correcting Words
      • Introduction
      • Stemming words
      • Lemmatizing words with WordNet
      • Translating text with Babelfish
      • Replacing words matching regular expressions
      • Removing repeating characters
      • Spelling correction with Enchant
      • Replacing synonyms
      • Replacing negations with antonyms
      • Chapter 3: Text Classification
        • Introduction
        • Bag of Words feature extraction
        • Training a naive Bayes classifier
        • Training a decision tree classifier
        • Training a maximum entropy classifier
        • Measuring precision and recall of a classifier
        • Calculating high information words
        • Combining classifiers with voting
        • Classifying with multiple binary classifiers

        Jacob Perkins

        Jacob Perkins is the author of Packt’s Python Text Processing with NLTK 2.0 Cookbook, and a contributor to the Bad Data Handbook. He is the CTO and cofounder of Weotta, a natural-language-based search engine for local entertainment. He created http://text-processing.com, which demos NLTK functionality and provides natural language processing APIs. Jacob also writes about natural language processing and Python programming at http://streamhacker.com and you can follow him on Twitter - twitter.com/japerk

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        Quickly get to grips with Natural Language Processing – with Tokenizing Text, WordNet, and beyond 
        Learn how to replace and correct words to improve search engine results 
        Categorize documents and pieces of text and assign class labels
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        What you will learn from this book

         

         

        • Learn Text categorization and Topic identification
        • Use Stemming and Lemmatization to go beyond the usual spell checker
        • Replace negations with antonyms in your text
        • Tokenize words into lists of sentences and words, and gain an insight into WordNet
        • Train and manipulate different types of classifiers
        • Automatically correct spelling with Enchant
        • Translate text using Babelfish

         

         

        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.

        To find out more about upgrading to the full edition, visit www.packtpub.com/lite-editions and log into your account for offers and help. If you don’t have an account on PacktPub.com, visit today and set one up!

        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 NLTK for Natural Language Processing easy and straightforward.

         

        Who this book is 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 NLTK will also find it useful. Students of linguistics will find it invaluable.

         

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