train_data = brown_tagged_sents[:int(len(brown_tagged_sents) * 0.9)] test_data = brown_tagged_sents[int(len(brown_tagged_sents) * 0.9):] unigram_tagger = UnigramTagger(train_data, backoff=default_tagger) print(unigram_tagger.evaluate(test_data)) # 0.835841722316356 bigram_tagger = BigramTagger(train_data, backoff=unigram_tagger) print(bigram_tagger.evaluate(test_data)) # 0.8454101465164956 trigram_tagger = TrigramTagger(train_data, backoff=bigram_tagger) print(trigram_tagger.evaluate(test_data)) # 0.8427190272102063 regexp_tagger = RegexpTagger( [( r'^-?[0-9]+(.[0-9]+)?$', 'CD'), # cardinal numbers ( r'(The|the|A|a|An|an)$', 'AT'), # articles ( r'.*able$', 'JJ'), # adjectives ( r'.*ness$', 'NN'), # nouns formed from adj ( r'.*ly$', 'RB'), # adverbs ( r'.*s$', 'NNS'), # plural nouns ( r'.*ing$', 'VBG'), # gerunds (r'.*ed$', 'VBD'), # past tense verbs (r'.*', 'NN') # nouns (default) ]) print(regexp_tagger.evaluate(test_data)) # 0.31306687929831556
def train(self, templates=None, verbose=True): """Train a new Brill tagger.""" if templates is None: templates = brill.nltkdemo18() random.seed(len(self.tagged_data_list)) random.shuffle(self.tagged_data_list) cutoff = int(self.dev_size * self.train_size) training_data = self.tagged_data_list[:cutoff] test_data = self.tagged_data_list[cutoff:self.dev_size] # very simple regular expression tagger regex_tagger = RegexpTagger([(r'^-?[0-9]+(.[0-9]+)?$', 'PUNCT'), (r'.*', 'N')]) if verbose == True: print "Regular expression tagger accuracy:\n{}\n".format( regex_tagger.evaluate(test_data)) # unigram tagger unigram_tagger = UnigramTagger(train=training_data, backoff=regex_tagger) if verbose == True: print "Unigram tagger accuracy:\n{}\n".format( unigram_tagger.evaluate(test_data)) # bigram tagger bigram_tagger = BigramTagger(train=training_data, backoff=unigram_tagger) if verbose == True: print "Bigram tagger accuracy:\n{}\n".format( bigram_tagger.evaluate(test_data)) # trigram tagger trigram_tagger = TrigramTagger(train=training_data, backoff=bigram_tagger) if verbose == True: print "Trigram tagger accuracy:\n{}\n".format( trigram_tagger.evaluate(test_data)) # first iteration trainer = BrillTaggerTrainer(initial_tagger=trigram_tagger, templates=templates) brill_tagger = trainer.train(train_sents=training_data, max_rules=self.max_rules, min_score=self.min_score) if verbose == True: print "Initial Brill tagger accuracy:\n{}\n".format( brill_tagger.evaluate(test_data)) # folding for i in range(0, self.num_groups): # random splitting random.seed(len(self.tagged_data_list)) random.shuffle(self.tagged_data_list) cutoff = int(self.dev_size * self.train_size) training_data = self.tagged_data_list[:cutoff] test_data = self.tagged_data_list[cutoff:self.dev_size] # note that .train method returns a BrillTagger() object brill_tagger = trainer.train(train_sents=training_data, max_rules=self.max_rules, min_score=self.min_score) if verbose == True: print "Brill tagger accuracy, fold {}:\n{}\n".format( i + 1, brill_tagger.evaluate(test_data))
# Regex tagger >>>from nltk.tag.sequential import RegexpTagger >>>regexp_tagger = RegexpTagger( [( r'^-?[0-9]+(.[0-9]+)?$', 'CD'), # cardinal numbers ( r'(The|the|A|a|An|an)$', 'AT'), # articles ( r'.*able$', 'JJ'), # adjectives ( r'.*ness$', 'NN'), # nouns formed from adj ( r'.*ly$', 'RB'), # adverbs ( r'.*s$', 'NNS'), # plural nouns ( r'.*ing$', 'VBG'), # gerunds (r'.*ed$', 'VBD'), # past tense verbs (r'.*', 'NN') # nouns (default) ]) >>>print regexp_tagger.evaluate(test_data) # NER tagger >>>import nltk >>>from nltk import ne_chunk >>>from nltk import word_tokenize >>>sent = "Mark is studying at Stanford University in California" >>>print(ne_chunk(nltk.pos_tag(word_tokenize(sent)), binary=False)) # NER stanford tagger >>>from nltk.tag.stanford import NERTagger >>>st = NERTagger('<PATH>/stanford-ner/classifiers/all.3class.distsim.crf.ser.gz',... '<PATH>/stanford-ner/stanford-ner.jar') # <PATH> will be the relative path where you downloaded the tagger
def train(self, templates=None, verbose=True): """Train a new Brill tagger.""" if templates is None: templates = brill.nltkdemo18() random.seed(len(self.tagged_data_list)) random.shuffle(self.tagged_data_list) cutoff = int(self.dev_size * self.train_size) training_data = self.tagged_data_list[:cutoff] test_data = self.tagged_data_list[cutoff:self.dev_size] # very simple regular expression tagger regex_tagger = RegexpTagger([ (r'^-?[0-9]+(.[0-9]+)?$', 'PUNCT'), (r'.*', 'N') ]) if verbose == True: print "Regular expression tagger accuracy:\n{}\n".format( regex_tagger.evaluate(test_data)) # unigram tagger unigram_tagger = UnigramTagger(train=training_data, backoff=regex_tagger) if verbose == True: print "Unigram tagger accuracy:\n{}\n".format( unigram_tagger.evaluate(test_data)) # bigram tagger bigram_tagger = BigramTagger(train=training_data, backoff=unigram_tagger) if verbose == True: print "Bigram tagger accuracy:\n{}\n".format( bigram_tagger.evaluate(test_data)) # trigram tagger trigram_tagger = TrigramTagger(train=training_data, backoff=bigram_tagger) if verbose == True: print "Trigram tagger accuracy:\n{}\n".format( trigram_tagger.evaluate(test_data)) # first iteration trainer = BrillTaggerTrainer(initial_tagger=trigram_tagger, templates=templates) brill_tagger = trainer.train(train_sents=training_data, max_rules=self.max_rules, min_score=self.min_score) if verbose == True: print "Initial Brill tagger accuracy:\n{}\n".format( brill_tagger.evaluate(test_data)) # folding for i in range(0, self.num_groups): # random splitting random.seed(len(self.tagged_data_list)) random.shuffle(self.tagged_data_list) cutoff = int(self.dev_size * self.train_size) training_data = self.tagged_data_list[:cutoff] test_data = self.tagged_data_list[cutoff:self.dev_size] # note that .train method returns a BrillTagger() object brill_tagger = trainer.train(train_sents=training_data, max_rules=self.max_rules, min_score=self.min_score) if verbose == True: print "Brill tagger accuracy, fold {}:\n{}\n".format( i+1, brill_tagger.evaluate(test_data))