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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

Chunking and chinking with regular expressions

Using modified regular expressions, we can define chunk patterns. These are patterns of part-of-speech tags that define what kinds of words make up a chunk. We can also define patterns for what kinds of words should not be in a chunk. These unchunked words are known as chinks.

A ChunkRule class specifies what to include in a chunk, while a ChinkRule class specifies what to exclude from a chunk. In other words, chunking creates chunks, while chinking breaks up those chunks.

Getting ready

We first need to know how to define chunk patterns. These are modified regular expressions designed to match sequences of part-of-speech tags. An individual tag is specified by surrounding angle brackets, such as <NN> to match a noun tag. Multiple tags can then be combined, as in <DT><NN> to match a determiner followed by a noun. Regular expression syntax can be used within the angle brackets to match individual tag patterns, so you can do ...

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