def demo_eval(chunkparser, text):
"""
Demonstration code for evaluating a chunk parser, using a
C{ChunkScore}. This function assumes that C{text} contains one
sentence per line, and that each sentence has the form expected by
C{tree.chunk}. It runs the given chunk parser on each sentence in
the text, and scores the result. It prints the final score
(precision, recall, and f-measure); and reports the set of chunks
that were missed and the set of chunks that were incorrect. (At
most 10 missing chunks and 10 incorrect chunks are reported).
@param chunkparser: The chunkparser to be tested
@type chunkparser: C{ChunkParserI}
@param text: The chunked tagged text that should be used for
evaluation.
@type text: C{string}
"""
from nltk import chunk, Tree
# Evaluate our chunk parser.
chunkscore = chunk.ChunkScore()
for sentence in text.split('\n'):
print sentence
sentence = sentence.strip()
if not sentence: continue
gold = chunk.tagstr2tree(sentence)
tokens = gold.leaves()
test = chunkparser.parse(Tree('S', tokens), trace=1)
chunkscore.score(gold, test)
print
print '/' + ('=' * 75) + '\\'
print 'Scoring', chunkparser
print('-' * 77)
print 'Precision: %5.1f%%' % (chunkscore.precision() * 100), ' ' * 4,
print 'Recall: %5.1f%%' % (chunkscore.recall() * 100), ' ' * 6,
print 'F-Measure: %5.1f%%' % (chunkscore.f_measure() * 100)
# Missed chunks.
if chunkscore.missed():
print 'Missed:'
missed = chunkscore.missed()
for chunk in missed[:10]:
print ' ', ' '.join(c.__str__() for c in chunk)
if len(chunkscore.missed()) > 10:
print ' ...'
# Incorrect chunks.
if chunkscore.incorrect():
print 'Incorrect:'
incorrect = chunkscore.incorrect()
for chunk in incorrect[:10]:
print ' ', ' '.join(c.__str__() for c in chunk)
if len(chunkscore.incorrect()) > 10:
print ' ...'
print '\\' + ('=' * 75) + '/'
print
def demo():
s = "[ Pierre/NNP Vinken/NNP ] ,/, [ 61/CD years/NNS ] old/JJ ,/, will/MD join/VB [ the/DT board/NN ] ./."
from nltk import chunk
t = chunk.tagstr2tree(s, chunk_node='NP')
print t.pprint()
print
s = """
These DT B-NP
research NN I-NP
protocols NNS I-NP
offer VBP B-VP
to TO B-PP
the DT B-NP
patient NN I-NP
not RB O
only RB O
the DT B-NP
very RB I-NP
best JJS I-NP
therapy NN I-NP
which WDT B-NP
we PRP B-NP
have VBP B-VP
established VBN I-VP
today NN B-NP
but CC B-NP
also RB I-NP
the DT B-NP
hope NN I-NP
of IN B-PP
something NN B-NP
still RB B-ADJP
better JJR I-ADJP
. . O
"""
conll_tree = conllstr2tree(s, chunk_types=('NP', 'PP'))
print conll_tree.pprint()
# Demonstrate CoNLL output
print "CoNLL output:"
print chunk.tree2conllstr(conll_tree)
print
def demo():
s = "[ Pierre/NNP Vinken/NNP ] ,/, [ 61/CD years/NNS ] old/JJ ,/, will/MD join/VB [ the/DT board/NN ] ./."
from nltk import chunk
t = chunk.tagstr2tree(s, chunk_node='NP')
print t.pprint()
print
s = """
These DT B-NP
research NN I-NP
protocols NNS I-NP
offer VBP B-VP
to TO B-PP
the DT B-NP
patient NN I-NP
not RB O
only RB O
the DT B-NP
very RB I-NP
best JJS I-NP
therapy NN I-NP
which WDT B-NP
we PRP B-NP
have VBP B-VP
established VBN I-VP
today NN B-NP
but CC B-NP
also RB I-NP
the DT B-NP
hope NN I-NP
of IN B-PP
something NN B-NP
still RB B-ADJP
better JJR I-ADJP
. . O
"""
conll_tree = conllstr2tree(s, chunk_types=('NP', 'PP'))
print conll_tree.pprint()
# Demonstrate CoNLL output
print "CoNLL output:"
print chunk.tree2conllstr(conll_tree)
print
def demo_eval(chunkparser, text):
"""
Demonstration code for evaluating a chunk parser, using a
C{ChunkScore}. This function assumes that C{text} contains one
sentence per line, and that each sentence has the form expected by
C{tree.chunk}. It runs the given chunk parser on each sentence in
the text, and scores the result. It prints the final score
(precision, recall, and f-measure); and reports the set of chunks
that were missed and the set of chunks that were incorrect. (At
most 10 missing chunks and 10 incorrect chunks are reported).
@param chunkparser: The chunkparser to be tested
@type chunkparser: C{ChunkParserI}
@param text: The chunked tagged text that should be used for
evaluation.
@type text: C{string}
"""
from nltk import chunk, Tree
# Evaluate our chunk parser.
chunkscore = chunk.ChunkScore()
for sentence in text.split('\n'):
print sentence
sentence = sentence.strip()
if not sentence: continue
gold = chunk.tagstr2tree(sentence)
tokens = gold.leaves()
test = chunkparser.parse(Tree('S', tokens), trace=1)
chunkscore.score(gold, test)
print
print '/'+('='*75)+'\\'
print 'Scoring', chunkparser
print ('-'*77)
print 'Precision: %5.1f%%' % (chunkscore.precision()*100), ' '*4,
print 'Recall: %5.1f%%' % (chunkscore.recall()*100), ' '*6,
print 'F-Measure: %5.1f%%' % (chunkscore.f_measure()*100)
# Missed chunks.
if chunkscore.missed():
print 'Missed:'
missed = chunkscore.missed()
for chunk in missed[:10]:
print ' ', ' '.join(c.__str__() for c in chunk)
if len(chunkscore.missed()) > 10:
print ' ...'
# Incorrect chunks.
if chunkscore.incorrect():
print 'Incorrect:'
incorrect = chunkscore.incorrect()
for chunk in incorrect[:10]:
print ' ', ' '.join(c.__str__() for c in chunk)
if len(chunkscore.incorrect()) > 10:
print ' ...'
print '\\'+('='*75)+'/'
print
def _read_block(self, stream):
return [tagstr2tree(t) for t in read_blankline_block(stream)]
def _read_block(self, stream):
return [tagstr2tree(t) for t in read_blankline_block(stream)]
from nltk import chunk
tagged_text = """
The/DT market/NN for/IN system-management/NN software/NN for/IN
Digital/NNP 's/POS hardware/NN is/VBZ fragmented/JJ enough/RB
that/IN a/DT giant/NN such/JJ as/IN Computer/NNP Associates/NNPS
should/MD do/VB well/RB there/RB ./.
"""
input = chunk.tagstr2tree(tagged_text)
print input
cp = chunk.RegexpParser("NP: {<DT><NN>}")
print cp.parse(input)
from nltk import chunk
tagged_text = """
The/DT market/NN for/IN system-management/NN software/NN for/IN
Digital/NNP 's/POS hardware/NN is/VBZ fragmented/JJ enough/RB
that/IN a/DT giant/NN such/JJ as/IN Computer/NNP Associates/NNPS
should/MD do/VB well/RB there/RB ./.
"""
input = chunk.tagstr2tree(tagged_text)
print input
cp = chunk.RegexpParser("NP: {<DT><NN>}")
print cp.parse(input)
