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NLP | Chunking and chinking with RegEx

  • Last Updated : 27 Sep, 2021

Chunk extraction or partial parsing is a process of meaningful extracting short phrases from the sentence (tagged with Part-of-Speech). 
Chunks are made up of words and the kinds of words are defined using the part-of-speech tags. One can even define a pattern or words that can’t be a part of chuck and such words are known as chinks. A ChunkRule class specifies what words or patterns to include and exclude in a chunk. 

Defining Chunk patterns : 
Chuck patterns are normal regular expressions which are modified and designed to match the part-of-speech tag designed to match sequences of part-of-speech tags. Angle brackets are used to specify an individual tag for example – to match a noun tag. One can define multiple tags in the same way. 

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Code #1 : Converting chunks to RegEx Pattern. 


# Laading Library
from nltk.chunk.regexp import tag_pattern2re_pattern
# Chunk Pattern to RegEx Pattern
print("Chunk Pattern : ", tag_pattern2re_pattern('<DT>?<NN.*>+'))

Output : 

Chunk Pattern :  ()?(<(NN[^\{\}]*)>)+

Curly Braces are used to specify a chunk like {} and to specify the chink pattern one can just flip the braces }{. For a particular phrase type, these rules (chunk and a chink pattern) can be combined into grammar.

Code #2 : Parsing the sentence with RegExParser.  


from nltk.chunk import RegexpParser
# Introducing the Pattern
chunker = RegexpParser(r'''
chunker.parse([('the', 'DT'), ('book', 'NN'), (
    'has', 'VBZ'), ('many', 'JJ'), ('chapters', 'NNS')])

Output : 

Tree('S', [Tree('NP', [('the', 'DT'), ('book', 'NN')]), ('has', 'VBZ'), 
Tree('NP', [('many', 'JJ'), ('chapters', 'NNS')])])


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