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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

Swapping verb phrases


Swapping the words around a verb can eliminate the passive voice from particular phrases. For example, the book was great can be transformed into the great book. This kind of normalization can also help with frequency analysis, by counting two apparently different phrases as the same phrase.

How to do it...

In transforms.py is a function called swap_verb_phrase(). It swaps the right-hand side of the chunk with the left-hand side, using the verb as the pivot point. It uses the first_chunk_index() function defined in the previous recipe to find the verb to pivot around.

def swap_verb_phrase(chunk):
  def vbpred(wt):
    word, tag = wt
    return tag != 'VBG' and tag.startswith('VB') and len(tag) > 2

  vbidx = first_chunk_index(chunk, vbpred)

  if vbidx is None:
    return chunk

  return chunk[vbidx+1:] + chunk[:vbidx]

Now we can see how it works on the part-of-speech tagged phrase the book was great:

>>> swap_verb_phrase([('the', 'DT'), ('book', 'NN'), ('was...
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