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Natural Language Processing with Python by Edward Loper, Steven Bird, Ewan Klein

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Recursion in Linguistic Structure

Building Nested Structure with Cascaded Chunkers

So far, our chunk structures have been relatively flat. Trees consist of tagged tokens, optionally grouped under a chunk node such as NP. However, it is possible to build chunk structures of arbitrary depth, simply by creating a multistage chunk grammar containing recursive rules. Example 7-9 has patterns for noun phrases, prepositional phrases, verb phrases, and sentences. This is a four-stage chunk grammar, and can be used to create structures having a depth of at most four.

Example 7-9. A chunker that handles NP, PP, VP, and S.

grammar = r"""
  NP: {<DT|JJ|NN.*>+}          # Chunk sequences of DT, JJ, NN
  PP: {<IN><NP>}               # Chunk prepositions followed by NP
  VP: {<VB.*><NP|PP|CLAUSE>+$} # Chunk verbs and their arguments
  CLAUSE: {<NP><VP>}           # Chunk NP, VP
  """
cp = nltk.RegexpParser(grammar)
sentence = [("Mary", "NN"), ("saw", "VBD"), ("the", "DT"), ("cat", "NN"),
    ("sit", "VB"), ("on", "IN"), ("the", "DT"), ("mat", "NN")]
>>> print cp.parse(sentence)
(S
  (NP Mary/NN)
  saw/VBD
  (CLAUSE
    (NP the/DT cat/NN)
    (VP sit/VB (PP on/IN (NP the/DT mat/NN)))))

Unfortunately this result misses the VP headed by saw. It has other shortcomings, too. Let’s see what happens when we apply this chunker to a sentence having deeper nesting. Notice that it fails to identify the VP chunk starting at 1.

>>> sentence = [("John", "NNP"), ("thinks", "VBZ"), ...

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