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Python 3 Text Processing with NLTK 3 Cookbook

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

Product type Book
Published in Aug 2014
Publisher
ISBN-13 9781782167853
Pages 304 pages
Edition 1st Edition
Languages
Author (1):
Jacob Perkins Jacob Perkins
Profile icon Jacob Perkins

Table of Contents (17) Chapters

Python 3 Text Processing with NLTK 3 Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Tokenizing Text and WordNet Basics 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 Penn Treebank Part-of-speech Tags
Index

Tagging with regular expressions


You can use regular expression matching to tag words. For example, you can match numbers with \d to assign the tag CD (which refers to a Cardinal number). Or you could match on known word patterns, such as the suffix "ing". There's a lot of flexibility here, but be careful of over-specifying since language is naturally inexact, and there are always exceptions to the rule.

Getting ready

For this recipe to make sense, you should be familiar with the regular expression syntax and Python's re module.

How to do it...

The RegexpTagger class expects a list of two tuples, where the first element in the tuple is a regular expression and the second element is the tag. The patterns shown in the following code can be found in tag_util.py:

patterns = [
  (r'^\d+$', 'CD'),
  (r'.*ing$', 'VBG'), # gerunds, i.e. wondering
  (r'.*ment$', 'NN'), # i.e. wonderment
  (r'.*ful$', 'JJ') # i.e. wonderful
]

Once you've constructed this list of patterns, you can pass it into RegexpTagger...

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