('chapters', 'NNS')]
# forth
chunker = RegexpParser(r'''
NP:
{<DT><NN.*><.*>*<NN.*>}
}<VB.*>{''')
print(chunker.parse(s))
# back
t = Tree('S', s)
cs = ChunkString(t)
print(cs)
ur = ChunkRule('<DT><NN.*><.*>*<NN.*>', 'chunk determiners and nouns')
ur.apply(cs)
print(cs)
ir = ChinkRule('<VB.*>', 'chink verbs')
ir.apply(cs)
print(cs)
print(cs.to_chunkstruct())
# cs.to_chunkstruct().draw()
chunker = RegexpChunkParser([ur, ir])
print(chunker.parse(t))
# set chunk name
chunker = RegexpChunkParser([ur, ir], chunk_label='CP')
print(chunker.parse(t))
# 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()
# forth
chunker = RegexpParser(r'''
NP:
{<DT><NN.*><.*>*<NN.*>}
}<VB.*>{'''
)
print(chunker.parse(s))
# back
t = Tree('S', s)
cs = ChunkString(t)
print(cs)
ur = ChunkRule('<DT><NN.*><.*>*<NN.*>', 'chunk determiners and nouns')
ur.apply(cs)
print(cs)
ir = ChinkRule('<VB.*>', 'chink verbs')
ir.apply(cs)
print(cs)
print(cs.to_chunkstruct())
# cs.to_chunkstruct().draw()
chunker = RegexpChunkParser([ur, ir])
print(chunker.parse(t))
# set chunk name
chunker = RegexpChunkParser([ur, ir], chunk_label='CP')
print(chunker.parse(t))