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Python 3 Text Processing with NLTK 3 Cookbook

You're reading from   Python 3 Text Processing with NLTK 3 Cookbook

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Product type Paperback
Published in Aug 2014
Publisher
ISBN-13 9781782167853
Length 304 pages
Edition 2nd Edition
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Author (1):
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Jacob Perkins Jacob Perkins
Author Profile Icon Jacob Perkins
Jacob Perkins
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Table of Contents (12) Chapters Close

Preface 1. Tokenizing Text and WordNet Basics FREE CHAPTER 2. Replacing and Correcting Words 3. Creating Custom Corpora 4. Part-of-speech Tagging 5. Extracting Chunks 6. Transforming Chunks and Trees 7. Text Classification 8. Distributed Processing and Handling Large Datasets 9. Parsing Specific Data Types A. Penn Treebank Part-of-speech Tags
Index

Replacing words matching regular expressions

Now, we are going to get into the process of replacing words. If stemming and lemmatization are a kind of linguistic compression, then word replacement can be thought of as error correction or text normalization.

In this recipe, we will replace words based on regular expressions, with a focus on expanding contractions. Remember when we were tokenizing words in Chapter 1, Tokenizing Text and WordNet Basics, and it was clear that most tokenizers had trouble with contractions? This recipe aims to fix this by replacing contractions with their expanded forms, for example, by replacing "can't" with "cannot" or "would've" with "would have".

Getting ready

Understanding how this recipe works will require a basic knowledge of regular expressions and the re module. The key things to know are matching patterns and the re.sub() function.

How to do it...

First, we need to define a number of replacement patterns...

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