def get_tagger():
d_tagger = nltk.DefaultTagger('NN')
re_tagger = nltk.RegexpTagger(patterns,backoff=d_tagger)
# train is the proportion of data used in training; the rest is reserved
# for testing.
print("Loading tagged data... ")
tagged_data = brown_tagged_sents
cutoff = int(1000*.8)
training_data = tagged_data[:cutoff]
gold_data = tagged_data[cutoff:1000]
testing_data = [[t[0] for t in sent] for sent in gold_data]
print("Done loading.")
bigram_tagger = tag.BigramTagger(training_data,backoff=re_tagger)
templates = [
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule, (1,1)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule, (2,2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule, (1,2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule, (1,3)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule, (1,1)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule, (2,2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule, (1,2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule, (1,3)),
brill.ProximateTokensTemplate(brill.ProximateTagsRule, (-1, -1), (1,1)),
brill.ProximateTokensTemplate(brill.ProximateWordsRule, (-1, -1), (1,1)),
]
trainer = brill.FastBrillTaggerTrainer(bigram_tagger, templates, 0)
brill_tagger = trainer.train(training_data, max_rules=100, min_score=3)
return brill_tagger
def train(self, sentence_list):
"""Trains the tagger from the tagged sentences provided
"""
noun_fallback = DefaultTagger('NN')
affix_fallback = AffixTagger(sentence_list, backoff=noun_fallback)
unigram_fallback = UnigramTagger(sentence_list, backoff=affix_fallback)
bigram_fallback = BigramTagger(sentence_list, backoff=unigram_fallback)
trigram_fallback = TrigramTagger(sentence_list,
backoff=bigram_fallback)
templates = [
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(1, 1)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(2, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(1, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(1, 3)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(1, 1)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(2, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(1, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(1, 3)),
brill.ProximateTokensTemplate(brill.ProximateTagsRule, (-1, -1),
(1, 1)),
brill.ProximateTokensTemplate(brill.ProximateWordsRule, (-1, -1),
(1, 1))
]
trainer = brill.FastBrillTaggerTrainer(trigram_fallback, templates)
self.tagger = trainer.train(sentence_list, max_rules=100, min_score=3)
def train_brill_tagger(initial_tagger, train_sents, **kwargs):
sym_bounds = [(1, 1), (2, 2), (1, 2), (1, 3)]
asym_bounds = [(-1, -1), (1, 1)]
templates = [
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
*sym_bounds),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
*sym_bounds),
brill.ProximateTokensTemplate(brill.ProximateTagsRule, *asym_bounds),
brill.ProximateTokensTemplate(brill.ProximateWordsRule, *asym_bounds)
]
trainer = brill.FastBrillTaggerTrainer(initial_tagger,
templates,
deterministic=True)
return trainer.train(train_sents, **kwargs)
def get_brill(train_sents=None):
import nltk, nltk.tag
from nltk.tag import brill
if train_sents is None:
train_sents = nltk.corpus.conll2000.tagged_sents(
) + nltk.corpus.brown.tagged_sents()
raubt_tagger = backoff_tagger(train_sents, [
nltk.tag.AffixTagger, nltk.tag.UnigramTagger, nltk.tag.BigramTagger,
nltk.tag.TrigramTagger
],
backoff=nltk.tag.RegexpTagger(word_patterns))
templates = [
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(1, 1)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(2, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(1, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(1, 3)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(1, 1)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(2, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(1, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(1, 3)),
brill.ProximateTokensTemplate(brill.ProximateTagsRule, (-1, -1),
(1, 1)),
brill.ProximateTokensTemplate(brill.ProximateWordsRule, (-1, -1),
(1, 1))
]
trainer = brill.FastBrillTaggerTrainer(raubt_tagger, templates)
braubt_tagger = trainer.train(train_sents, max_rules=100, min_score=3)
return braubt_tagger
def __init__(self, msr_data=True, msr_data_location=params.msr_dataset):
tagged_sents = nltk.corpus.indian.tagged_sents(fileids="bangla.pos")
filtered_sents1 = []
for i, sent in enumerate(tagged_sents):
try:
nltk.tag.UnigramTagger([sent])
filtered_sents1.append(sent)
except ValueError:
pass
filtered_sents2 = []
if msr_data == True:
cwdir = os.getcwd()
listing = os.listdir(msr_data_location)
os.chdir(msr_data_location)
for fileName in listing:
xmlTree = parse(fileName)
nodes = xmlTree.getElementsByTagName('sentence')
try:
for node in nodes:
pairs = node.childNodes[0].nodeValue.split()
pairs = map(lambda a: a.split("\\"), pairs)
pairs = map(
lambda (a, b):
(a.encode("utf-8"), b.encode().split(".")[0]),
pairs)
nltk.tag.UnigramTagger([pairs])
filtered_sents2.append(pairs)
except ValueError:
pass
os.chdir(cwdir)
total_set = filtered_sents1 + filtered_sents2
aubt_tagger = [
nltk.tag.AffixTagger, nltk.tag.UnigramTagger,
nltk.tag.BigramTagger, nltk.tag.TrigramTagger
]
brill_templates = [
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(1, 1)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(2, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(1, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(1, 3)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(1, 1)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(2, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(1, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(1, 3)),
brill.ProximateTokensTemplate(brill.ProximateTagsRule, (-1, -1),
(1, 1)),
brill.ProximateTokensTemplate(brill.ProximateWordsRule, (-1, -1),
(1, 1))
]
btrainer = nltk.tag.brill.FastBrillTaggerTrainer(
self.backoff_tagger(total_set, aubt_tagger), brill_templates)
self.tagger = btrainer.train(total_set, max_rules=300, min_score=3)
from nltk.tag import brill
from nltk.corpus import TaggedCorpusReader
from .Extras import carrega
from .AnotaCorpus import abre_etiquetador
from .ConstroiRUBT import EXEMPLO, SENTENCA
TEMPLATES = [
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule, (1, 1)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule, (2, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule, (1, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule, (1, 3)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule, (1, 1)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule, (2, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule, (1, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule, (1, 3)),
brill.ProximateTokensTemplate(brill.ProximateTagsRule, (-1, -1), (1, 1)),
brill.ProximateTokensTemplate(brill.ProximateWordsRule, (-1, -1), (1, 1))
]
INICIAL = carrega("AeliusRUBT.pkl")
def treina(expressao_regular,
etiquetador=INICIAL,
destino="BRUBT.pkl",
raiz=".",
codificacao="utf-8",
max_rules=100,
min_score=3):
inicial = abre_etiquetador(etiquetador)
corpus = TaggedCorpusReader(raiz, expressao_regular, encoding=codificacao)
def demo(num_sents=100,
max_rules=200,
min_score=2,
error_output="errors.out",
rule_output="rules.out",
randomize=False,
train=.8,
trace=3):
"""
Brill Tagger Demonstration
@param num_sents: how many sentences of training and testing data to use
@type num_sents: L{int}
@param max_rules: maximum number of rule instances to create
@type max_rules: L{int}
@param min_score: the minimum score for a rule in order for it to be considered
@type min_score: L{int}
@param error_output: the file where errors will be saved
@type error_output: L{string}
@param rule_output: the file where rules will be saved
@type rule_output: L{string}
@param randomize: whether the training data should be a random subset of the corpus
@type randomize: L{boolean}
@param train: the fraction of the the corpus to be used for training (1=all)
@type train: L{float}
@param trace: the level of diagnostic tracing output to produce (0-3)
@type trace: L{int}
"""
from nltk.corpus import treebank
from nltk import tag
from nltk.tag import brill
NN_CD_tagger = tag.Regexp([(r'^-?[0-9]+(.[0-9]+)?$', 'CD'), (r'.*', 'NN')])
# train is the proportion of data used in training; the rest is reserved
# for testing.
print "Loading tagged data..."
sents = []
for item in treebank.items:
sents.extend(treebank.tagged(item))
if randomize:
random.seed(len(sents))
random.shuffle(sents)
tagged_data = [t for s in sents[:num_sents] for t in s]
cutoff = int(len(tagged_data) * train)
training_data = tagged_data[:cutoff]
gold_data = tagged_data[cutoff:]
testing_data = [t[0] for t in gold_data]
# Unigram tagger
print "Training unigram tagger:",
u = tag.Unigram(backoff=NN_CD_tagger)
# NB training and testing are required to use a list-of-lists structure,
# so we wrap the flattened corpus data with the extra list structure.
u.train([training_data])
print("[accuracy: %f]" % tag.accuracy(u, [gold_data]))
# Brill tagger
templates = [
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(1, 1)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(2, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(1, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateTagsRule,
(1, 3)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(1, 1)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(2, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(1, 2)),
brill.SymmetricProximateTokensTemplate(brill.ProximateWordsRule,
(1, 3)),
brill.ProximateTokensTemplate(brill.ProximateTagsRule, (-1, -1),
(1, 1)),
brill.ProximateTokensTemplate(brill.ProximateWordsRule, (-1, -1),
(1, 1)),
]
#trainer = brill.FastBrillTrainer(u, templates, trace)
trainer = brill.BrillTrainer(u, templates, trace)
b = trainer.train(training_data, max_rules, min_score)
print
print("Brill accuracy: %f" % tag.accuracy(b, [gold_data]))
print("\nRules: ")
printRules = file(rule_output, 'w')
for rule in b.rules():
print(str(rule))
printRules.write(str(rule) + "\n\n")
testing_data = list(b.tag(testing_data))
el = errorList(gold_data, testing_data)
errorFile = file(error_output, 'w')
for e in el:
errorFile.write(e + "\n\n")
errorFile.close()
print "Done; rules and errors saved to %s and %s." % (rule_output,
error_output)
