Below are the steps involved for Chunking –
- Conversion of sentence to a flat tree.
- Creation of Chunk string using this tree.
- Creation of RegexpChunkParser by parsing the grammar using RegexpParser.
- Applying the created chunk rule to the ChunkString that matches the sentence into a chunk.
- Splitting the bigger chunk to a smaller chunk using the defined chunk rules.
- ChunkString is then converted back to tree, with two chunk subtrees.
Code #1: ChunkString getting modified by applying each rule.
Python3
# Loading Libraries from nltk.chunk.regexp import ChunkString, ChunkRule, ChinkRule from nltk.tree import Tree # ChunkString() starts with the flat tree tree = Tree('S', [('the', 'DT'), ('book', 'NN'), ('has', 'VBZ'), ('many', 'JJ'), ('chapters', 'NNS')]) # Initializing ChunkString() chunk_string = ChunkString(tree) print ("Chunk String : ", chunk_string) # Initializing ChunkRule chunk_rule = ChunkRule('<DT><NN.*><.*>*<NN.*>', 'chunk determiners and nouns') chunk_rule.apply(chunk_string) print ("\nApplied ChunkRule : ", chunk_string) # Another ChinkRule ir = ChinkRule('<VB.*>', 'chink verbs') ir.apply(chunk_string) print ("\nApplied ChinkRule : ", chunk_string, "\n") # Back to chunk sub-tree chunk_string.to_chunkstruct() |
Output:
Chunk String : <<DT> <NN> <VBZ> <JJ> <NNS>
Applied ChunkRule : {<DT> <NN> <VBZ> <JJ> <NNS>}
Applied ChinkRule : {<DT> <NN>} <VBZ> {<JJ> <NNS>}
Tree('S', [Tree('CHUNK', [('the', 'DT'), ('book', 'NN')]),
('has', 'VBZ'), Tree('CHUNK', [('many', 'JJ'), ('chapters', 'NNS')])])
Note: This code works exactly in the same manner as explained in the ChunkRule steps above.
Code #2: How to this task directly with RegexpChunkParser.
Python3
# Loading Libraries from nltk.chunk.regexp import ChunkString, ChunkRule, ChinkRule from nltk.tree import Tree from nltk.chunk import RegexpChunkParser # ChunkString() starts with the flat tree tree = Tree('S', [('the', 'DT'), ('book', 'NN'), ('has', 'VBZ'), ('many', 'JJ'), ('chapters', 'NNS')]) # Initializing ChunkRule chunk_rule = ChunkRule('<DT><NN.*><.*>*<NN.*>', 'chunk determiners and nouns') # Another ChinkRule chink_rule = ChinkRule('<VB.*>', 'chink verbs') # Applying RegexpChunkParser chunker = RegexpChunkParser([chunk_rule, chink_rule]) chunker.parse(tree) |
Output:
Tree('S', [Tree('CHUNK', [('the', 'DT'), ('book', 'NN')]),
('has', 'VBZ'), Tree('CHUNK', [('many', 'JJ'), ('chapters', 'NNS')])])
Code #3 : Parsing with different ChunkType.
Python3
# Loading Libraries from nltk.chunk.regexp import ChunkString, ChunkRule, ChinkRule from nltk.tree import Tree from nltk.chunk import RegexpChunkParser # ChunkString() starts with the flat tree tree = Tree('S', [('the', 'DT'), ('book', 'NN'), ('has', 'VBZ'), ('many', 'JJ'), ('chapters', 'NNS')]) # Initializing ChunkRule chunk_rule = ChunkRule('<DT><NN.*><.*>*<NN.*>', 'chunk determiners and nouns') # Another ChinkRule chink_rule = ChinkRule('<VB.*>', 'chink verbs') # Applying RegexpChunkParser chunker = RegexpChunkParser([chunk_rule, chink_rule], chunk_label ='CP') chunker.parse(tree) |
Output:
Tree('S', [Tree('CP', [('the', 'DT'), ('book', 'NN')]), ('has', 'VBZ'),
Tree('CP', [('many', 'JJ'), ('chapters', 'NNS')])])
