Natural Language Processing: Python and NLTK

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 ...