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  • Book Overview & Buying Python Text Processing with NLTK 2.0 Cookbook: LITE
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Python Text Processing with NLTK 2.0 Cookbook: LITE

Python Text Processing with NLTK 2.0 Cookbook: LITE

By : Jacob Perkins
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Python Text Processing with NLTK 2.0 Cookbook: LITE

Python Text Processing with NLTK 2.0 Cookbook: LITE

1 (1)
By: Jacob Perkins
Buy this Book

Overview of this book

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. 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.
Table of Contents (5 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Tokenizing Text and WordNet Basics
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1. Tokenizing Text and WordNet Basics
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Introduction
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Tokenizing text into sentences
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Tokenizing sentences into words
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Tokenizing sentences using regular expressions
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Filtering stopwords in a tokenized sentence
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Looking up synsets for a word in WordNet
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Looking up lemmas and synonyms in WordNet
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Calculating WordNet synset similarity
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Discovering word collocations
2
2. Replacing and Correcting Words
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2. Replacing and Correcting Words
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Introduction
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Stemming words
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Lemmatizing words with WordNet
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Translating text with Babelfish
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Replacing words matching regular expressions
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Removing repeating characters
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Spelling correction with Enchant
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Replacing synonyms
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Replacing negations with antonyms
3
3. Text Classification
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3. Text Classification
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Introduction
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Bag of Words feature extraction
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Training a naive Bayes classifier
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Training a decision tree classifier
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Training a maximum entropy classifier
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Measuring precision and recall of a classifier
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Calculating high information words
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Combining classifiers with voting
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Classifying with multiple binary classifiers
4
Index
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Index

Replacing synonyms

It is often useful to reduce the vocabulary of a text by replacing words with common synonyms. By compressing the vocabulary without losing meaning, you can save memory in cases such as frequency analysis and text indexing. Vocabulary reduction can also increase the occurrence of significant collocations, which was covered in the Discovering word collocations recipe of Chapter 1,

Getting ready

You will need to have a defined mapping of a word to its synonym. This is a simple controlled vocabulary. We will start by hardcoding the synonyms as a Python dictionary, then explore other options for storing synonym maps.

How to do it...

We'll first create a WordReplacer class in replacers.py that takes a word replacement mapping:

class WordReplacer(object):
	def __init__(self, word_map):
		self.word_map = word_map
	def replace(self, word):
		return self.word_map.get(word, word)

Then we can demonstrate its usage for simple word replacement:

>>> from replacers import wordReplacer...
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Python Text Processing with NLTK 2.0 Cookbook: LITE
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