def testLengthOfText(brownSection):
items = brown.items(brownSection)
textToken = brown.read(items[0])
for i in range(len(items)):
print "\n\nWeightedTaggedTScoreModel\n"
model = WeightedTaggedTScoreModel(textToken, SUBTOKENS='WORDS')
TextGenerator(model).generateWords(('the','at'),100)
textToken['WORDS'] = textToken['WORDS'] + brown.read(items[i+1])['WORDS']
def main():
print "\n\nWeightedTaggedTScoreModel\n"
items = brown.items('fiction: general')
textToken = brown.read(items[0])
for item in items[1:]:
textToken['WORDS'] = textToken['WORDS'] + brown.read(item)['WORDS']
model = WeightedTaggedTScoreModel(textToken, SUBTOKENS='WORDS')
TextGenerator(model).generateWords(('the','at'), 200)
print "\n\nWeightedTScoreModel...\n"
model = WeightedTScoreModel(textToken, SUBTOKENS='WORDS')
TextGenerator(model).generateWords('the',200)
def buildTrainTokens(self, texts=10):
'''
Tokenize texts from the Brown Corpus.
'''
from nltk.corpus import brown
train_tokens = []
for item in brown.items()[:texts]:
train_tokens.append(brown.read(item))
self.train_tokens = train_tokens
def load_pos():
from nltk.corpus import brown
tagged_tokens = []
for item in brown.items()[:5]:
tagged_tokens.append(brown.read(item))
tag_set = [
"'", "''", '(', ')', '*', ',', '.', ':', '--', '``', 'abl', 'abn',
'abx', 'ap', 'ap$', 'at', 'be', 'bed', 'bedz', 'beg', 'bem', 'ben',
'ber', 'bez', 'cc', 'cd', 'cd$', 'cs', 'do', 'dod', 'doz', 'dt', 'dt$',
'dti', 'dts', 'dtx', 'ex', 'fw', 'hv', 'hvd', 'hvg', 'hvn', 'hvz',
'in', 'jj', 'jjr', 'jjs', 'jjt', 'md', 'nn', 'nn$', 'nns', 'nns$',
'np', 'np$', 'nps', 'nps$', 'nr', 'nr$', 'od', 'pn', 'pn$', 'pp$',
'ppl', 'ppls', 'ppo', 'pps', 'ppss', 'ql', 'qlp', 'rb', 'rb$', 'rbr',
'rbt', 'rp', 'to', 'uh', 'vb', 'vbd', 'vbg', 'vbn', 'vbz', 'wdt',
'wp$', 'wpo', 'wps', 'wql', 'wrb'
]
sequences = []
sequence = []
symbols = {}
start_re = re.compile(r'[^-*+]*')
for token in tagged_tokens:
for sub_token in token['WORDS']:
sequence.append(sub_token)
# make words lower case
sub_token['TEXT'] = sub_token['TEXT'].lower()
symbols[sub_token['TEXT']] = 1
m = start_re.match(sub_token['TAG'])
# cleanup the tag
tag = m.group(0)
if tag in tag_set:
sub_token['TAG'] = tag
else:
sub_token['TAG'] = '*'
# split on the period tag
if sub_token['TAG'] == '.':
sequences.append(Token(SUBTOKENS=sequence))
sequence = []
return sequences, tag_set, symbols.keys()
def load_pos():
from nltk.corpus import brown
tagged_tokens = []
for item in brown.items()[:5]:
tagged_tokens.append(brown.read(item))
tag_set = [
"'",
"''",
"(",
")",
"*",
",",
".",
":",
"--",
"``",
"abl",
"abn",
"abx",
"ap",
"ap$",
"at",
"be",
"bed",
"bedz",
"beg",
"bem",
"ben",
"ber",
"bez",
"cc",
"cd",
"cd$",
"cs",
"do",
"dod",
"doz",
"dt",
"dt$",
"dti",
"dts",
"dtx",
"ex",
"fw",
"hv",
"hvd",
"hvg",
"hvn",
"hvz",
"in",
"jj",
"jjr",
"jjs",
"jjt",
"md",
"nn",
"nn$",
"nns",
"nns$",
"np",
"np$",
"nps",
"nps$",
"nr",
"nr$",
"od",
"pn",
"pn$",
"pp$",
"ppl",
"ppls",
"ppo",
"pps",
"ppss",
"ql",
"qlp",
"rb",
"rb$",
"rbr",
"rbt",
"rp",
"to",
"uh",
"vb",
"vbd",
"vbg",
"vbn",
"vbz",
"wdt",
"wp$",
"wpo",
"wps",
"wql",
"wrb",
]
sequences = []
sequence = []
symbols = {}
start_re = re.compile(r"[^-*+]*")
for token in tagged_tokens:
for sub_token in token["WORDS"]:
sequence.append(sub_token)
# make words lower case
sub_token["TEXT"] = sub_token["TEXT"].lower()
symbols[sub_token["TEXT"]] = 1
m = start_re.match(sub_token["TAG"])
# cleanup the tag
tag = m.group(0)
if tag in tag_set:
sub_token["TAG"] = tag
else:
sub_token["TAG"] = "*"
# split on the period tag
if sub_token["TAG"] == ".":
sequences.append(Token(SUBTOKENS=sequence))
sequence = []
return sequences, tag_set, symbols.keys()
# aprendizado jah estao demarcados com as suas tags. Sendo assim,
# estes corpus podem ser utilizados tambem para a avaliacao dos
# algoritmos de tags.
#
# um exemplo simples
from nltk.tokenizer import *
from nltk.tagger import *
from nltk.corpus import brown
# fase de treinamento
train_tokens = []
for item in brown.items()[:10]:
train_tokens.append(brown.read(item))
mytagger = UnigramTagger(SUBTOKENS='WORDS')
for tok in train_tokens:
mytagger.train(tok)
# utilizacao
text_token = Token(TEXT="John saw the book on the table")
WhitespaceTokenizer(SUBTOKENS='WORDS').tokenize(text_token)
mytagger.tag(text_token)
print text_token
#
# depois de criado o mytagger podemos testa-lo
# com um corpus. Por exemplo, train_tokens
#
# Anteriormente, nos tentamos inferir a tag de um atomo
# baseado apenas no texto do atomo.
# Agora vamos usar informacoes sobre o contexto de um atomo
# (palavras que estao ao seu entorno) para inferir a sua tag.
#
#
# Bigram Taggers: utiliza sempre a palavra mais proxima do atomo
# Geralmente, a anterior
#
# N-gram Taggers: utiliza sempre a n palavra mais proxima do atomo
# Geralmente, a n-palavra anterior.
#
from nltk.tokenizer import *
from nltk.tagger import *
from nltk.corpus import brown
tagger = NthOrderTagger(3, SUBTOKENS='WORDS') # 3rd order tagger
for item in brown.items()[:10]:
tok = brown.read(item)
tagger.train(tok)
# utilizacao
text_token = Token(TEXT="John saw the book on the table")
WhitespaceTokenizer(SUBTOKENS='WORDS').tokenize(text_token)
tagger.tag(text_token)
print text_token
def demo(num_files=20):
"""
A simple demonstration function for the C{Tagger} classes. It
constructs a C{BackoffTagger} using a 2nd order C{NthOrderTagger},
a 1st order C{NthOrderTagger}, a 0th order C{NthOrderTagger}, and
an C{DefaultTagger}. It trains and tests the tagger using the
brown corpus.
@type num_files: C{int}
@param num_files: The number of files that should be used for
training and for testing. Two thirds of these files will be
used for training. All files are randomly selected
(I{without} replacement) from the brown corpus. If
C{num_files>=500}, then all 500 files will be used.
@rtype: None
"""
from nltk.corpus import brown
import sys, random
num_files = max(min(num_files, 500), 3)
# Get a randomly sorted list of files in the brown corpus.
items = list(brown.items())
random.shuffle(items)
# Tokenize the training files.
print '='*75
sys.stdout.write('Reading training data'); sys.stdout.flush()
train_tokens = []
num_words = 0
for item in items[:num_files*2/3]:
sys.stdout.write('.'); sys.stdout.flush()
train_tokens.append(brown.read(item))
num_words += len(train_tokens[-1]['WORDS'])
print '\n Read in %d words for training' % num_words
print 'Training taggers.'
# Create a default tagger
default_tagger = DefaultTagger('nn', SUBTOKENS='WORDS')
print ' Training unigram tagger...'
t0 = UnigramTagger(SUBTOKENS='WORDS')
for tok in train_tokens: t0.train(tok)
print ' Training bigram tagger...'
t1 = NthOrderTagger(1, SUBTOKENS='WORDS')
for tok in train_tokens: t1.train(tok)
print ' Training trigram tagger...'
t2 = NthOrderTagger(2, SUBTOKENS='WORDS')
for tok in train_tokens: t2.train(tok)
# Delete train_tokens, because it takes up lots of memory.
del train_tokens
# Tokenize the testing files
test_tokens = []
num_words = 0
sys.stdout.write('Reading testing data'); sys.stdout.flush()
for item in items[num_files*2/3:num_files]:
sys.stdout.write('.'); sys.stdout.flush()
test_tok = brown.read(item)
num_words += len(test_tok['WORDS'])
test_tokens.append(test_tok)
print '\n Read in %d words for testing' % num_words
# Run the taggers. For t0, t1, and t2, back-off to DefaultTagger.
# This is especially important for t1 and t2, which count on
# having known tags as contexts; if they get a context containing
# None, then they will generate an output of None, and so all
# words will get tagged a None.
print '='*75
print 'Running the taggers on test data...'
print ' Default (nn) tagger: ',
sys.stdout.flush()
_demo_tagger(test_tokens, default_tagger)
print ' Unigram tagger: ',
sys.stdout.flush()
_demo_tagger(test_tokens, BackoffTagger([t0, default_tagger], SUBTOKENS='WORDS'))
print ' Bigram tagger: ',
sys.stdout.flush()
_demo_tagger(test_tokens, BackoffTagger([t1, t0, default_tagger], SUBTOKENS='WORDS'))
print ' Trigram tagger: ',
sys.stdout.flush()
trigram = BackoffTagger([t2, t1, t0, default_tagger], SUBTOKENS='WORDS')
_demo_tagger(test_tokens, trigram)
print '\nUsage statistics for the trigram tagger:\n'
trigram.print_usage_stats()
print '='*75