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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
Languages
Tools
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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

Correcting verb forms


It's fairly common to find incorrect verb forms in real-world language. For example, the correct form of is our children learning? is are our children learning? The verb is should only be used with singular nouns, while are is for plural nouns, such as children. We can correct these mistakes by creating verb correction mappings that are used depending on whether there's a plural or singular noun in the chunk.

Getting ready

We first need to define the verb correction mappings in transforms.py. We'll create two mappings, one for plural to singular and another for singular to plural:

plural_verb_forms = {
  ('is', 'VBZ'): ('are', 'VBP'),
  ('was', 'VBD'): ('were', 'VBD')
}

singular_verb_forms = {
  ('are', 'VBP'): ('is', 'VBZ'),
  ('were', 'VBD'): ('was', 'VBD')
}

Each mapping has a tagged verb that maps to another tagged verb. These initial mappings cover the basics of mapping is to are, was to were, and vice versa.

How to do it...

In transforms.py is a function called correct_verbs...

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