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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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Toc

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

Chaining chunk transformations


The transform functions defined in the previous recipes can be chained together to normalize chunks. The resulting chunks are often shorter with no loss of meaning.

How to do it...

In transforms.py is the function transform_chunk(). It takes a single chunk and an optional list of transform functions. It calls each transform function on the chunk, one at a time, and returns the final chunk:

def transform_chunk(chunk, chain=[filter_insignificant, swap_verb_phrase, swap_infinitive_phrase, singularize_plural_noun], trace=0):
  for f in chain:
    chunk = f(chunk)

    if trace:
      print f.__name__, ':', chunk

  return chunk

Using it on the phrase the book of recipes is delicious, we get delicious recipe book:

>>> from transforms import transform_chunk
>>> transform_chunk([('the', 'DT'), ('book', 'NN'), ('of', 'IN'), ('recipes', 'NNS'), ('is', 'VBZ'), ('delicious', 'JJ')])
[('delicious', 'JJ'), ('recipe', 'NN'), ('book', 'NN')]

How it works...

The...

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