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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

Creating a shallow tree


In the previous recipe, we flattened a deep Tree by only keeping the lowest level subtrees. In this recipe, we'll keep only the highest level subtrees instead.

How to do it...

We'll be using the first parsed sentence from the treebank corpus as our example. Recall from the previous recipe that the sentence Tree looks like this:

The shallow_tree() function defined in transforms.py eliminates all the nested subtrees, keeping only the top subtree labels:

from nltk.tree import Tree

def shallow_tree(tree):
  children = []

  for t in tree:
    if t.height() < 3:
      children.extend(t.pos())
    else:
      children.append(Tree(t.label(), t.pos()))

  return Tree(tree.label(), children)

Using it on the first parsed sentence in treebank results in a Tree with only two subtrees:

>>> from transforms import shallow_tree
>>> shallow_tree(treebank.parsed_sents()[0])
Tree('S', [Tree('NP-SBJ', [('Pierre', 'NNP'), ('Vinken', 'NNP'), (',', ','), ('61', 'CD'), (...
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