def make_pos_model(model_type):
now = time.time()
reader = TaggedCorpusReader('.', 'greek_training_set.pos')
train_sents = reader.tagged_sents()
if model_type == 'unigram':
tagger = UnigramTagger(train_sents)
file = 'unigram.pickle'
elif model_type == 'bigram':
tagger = BigramTagger(train_sents)
file = 'bigram.pickle'
elif model_type == 'trigram':
tagger = TrigramTagger(train_sents)
file = 'trigram.pickle'
elif model_type == 'backoff':
tagger1 = UnigramTagger(train_sents)
tagger2 = BigramTagger(train_sents, backoff=tagger1)
tagger = TrigramTagger(train_sents, backoff=tagger2)
file = '123grambackoff.pickle'
elif model_type == 'tnt':
tagger = tnt.TnT()
tagger.train(train_sents)
file = 'tnt.pickle'
else:
print('Invalid model_type.')
_dir = os.path.expanduser('~/greek_models_cltk/taggers/pos')
path = os.path.join(_dir, file)
with open(path, 'wb') as f:
pickle.dump(tagger, f)
print('Completed training {0} model in {1} seconds to {2}.'.format(
model_type,
time.time() - now, path))
def train(self):
self.re_tagger = nltk.RegexpTagger(self.patterns)
self.bi_tagger = BigramTagger(brown.tagged_sents(),
backoff=self.re_tagger)
self.tri_tagger = TrigramTagger(brown.tagged_sents(),
backoff=self.bi_tagger)
def __init__(self, idiom):
self.tagger0 = DefaultTagger('N')
self.tagger1 = UnigramTagger(None, self.tagger0)
self.tagger2 = BigramTagger(None, self.tagger1)
self.lang = os.path.abspath('FriggAnswer')+'/pickle/'
#self.lang = os.path.abspath('pickle')+'\\'
self.loadIdiom(idiom)
def get_pos_tagger(self):
from nltk.corpus import brown
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 adjectives
(r'.*ly$', 'RB'), # adverbs
(r'.*s$', 'NNS'), # plural nouns
(r'.*ing$', 'VBG'), # gerunds
(r'.*ed$', 'VBD'), # past tense verbs
(r'.*', 'NN'), # nouns (default)
])
brown_train = brown.tagged_sents(categories='news')
unigram_tagger = UnigramTagger(brown_train, backoff=regexp_tagger)
bigram_tagger = BigramTagger(brown_train, backoff=unigram_tagger)
trigram_tagger = TrigramTagger(brown_train, backoff=bigram_tagger)
# Override particular words
main_tagger = RegexpTagger(
[(r'(A|a|An|an)$', 'ex_quant'),
(r'(Every|every|All|all)$', 'univ_quant')],
backoff=trigram_tagger,
)
return main_tagger
def traintest_bigram_trigram_tagger(self):
from nltk.tag import DefaultTagger,UnigramTagger, BigramTagger, TrigramTagger
from nltk.corpus import treebank
test_sents = treebank.tagged_sents()[3000:]
train_sents = treebank.tagged_sents()[:3000]
print 'trainging bigramTagger'
bitagger = BigramTagger(train_sents)
print 'evaluation bitagger'
print bitagger.evaluate(test_sents)
print 'trainging trigram Tagger'
tritagger = TrigramTagger(train_sents)
print 'evaluation bitagger'
print tritagger.evaluate(test_sents)
print 'tagging'
def get_pos_tagger(self):
from nltk.corpus import brown
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 adjectives
(r".*ly$", "RB"), # adverbs
(r".*s$", "NNS"), # plural nouns
(r".*ing$", "VBG"), # gerunds
(r".*ed$", "VBD"), # past tense verbs
(r".*", "NN"), # nouns (default)
]
)
brown_train = brown.tagged_sents(categories="news")
unigram_tagger = UnigramTagger(brown_train, backoff=regexp_tagger)
bigram_tagger = BigramTagger(brown_train, backoff=unigram_tagger)
trigram_tagger = TrigramTagger(brown_train, backoff=bigram_tagger)
# Override particular words
main_tagger = RegexpTagger(
[(r"(A|a|An|an)$", "ex_quant"), (r"(Every|every|All|all)$", "univ_quant")],
backoff=trigram_tagger,
)
return main_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 lexical(tokens):
print "\n"
print "Step 2: Lexical Analysis\n"
print "Essentially refers to dictionary and obtains the properties of the word"
print "Part-Of-Speech tagging"
print "The tagset is:\n"
tag = DefaultTagger('NN')
tagg = UnigramTagger(train_sent, backoff=tag)
tagger = BigramTagger(train_sent, backoff=tagg)
tagtokens = tagger.tag(tokens)
for token, tag in tagtokens:
print token + "->" + tag
print "\n"
print "The acurracy of the trained pos tagger is:"
print tagger.evaluate(test_sents)
return tagtokens
def train_tagger(language, model_type, feature, train_sents):
if model_type == 'unigram':
tagger = UnigramTagger(train_sents)
elif model_type == 'bigram':
tagger = BigramTagger(train_sents)
elif model_type == 'trigram':
tagger = TrigramTagger(train_sents)
elif model_type == 'backoff':
tagger1 = UnigramTagger(train_sents)
tagger2 = BigramTagger(train_sents, backoff=tagger1)
tagger = TrigramTagger(train_sents, backoff=tagger2)
elif model_type == 'crf':
tagger = CRFTagger()
tagger.train(train_sents,
'taggers/{0}/{1}/crf.pickle'.format(language, feature))
elif model_type == 'perceptron':
tagger = PerceptronTagger(load=False)
tagger.train(train_sents)
return tagger
def train_tagger(tagger_name):
train_sents = treebank.tagged_sents()[:5000]
if tagger_name == "TnT" or tagger_name == 'tagger':
trained_tagger = tnt.TnT()
trained_tagger.train(train_sents)
else:
tagger1 = DefaultTagger('NN')
tagger2 = TrigramTagger(train_sents, backoff=tagger1)
tagger3 = BigramTagger(train_sents, backoff=tagger2)
trained_tagger = UnigramTagger(train_sents, backoff=tagger3)
return trained_tagger
def ngram_tagger(tagged_sents):
patterns = [(r'''(b|c|d|f|g|h|j|k|l|m|n||p|q|r|s|t|v|w|x|z)e
(b|c|d|f|g|h|j|k|l|m|n||p|q|r|s|t|v|w|x|z)''', 'MORA'),
(r'.*(a|e|i|o|u|ä|î|ô|ü)(a|e|i|o|u|ä|î|ô|ü)', 'DOPPEL'),
(r'.*', 'MORA_HAUPT')] # default
regex_tagger = nltk.RegexpTagger(patterns)
tagger1 = UnigramTagger(tagged_sents, backoff=regex_tagger)
# cutoff = 3, if necessary
tagger2 = BigramTagger(tagged_sents, backoff=tagger1)
tagger3 = TrigramTagger(tagged_sents, backoff=tagger2)
return tagger3
def test_ngram_taggers(self):
unitagger = UnigramTagger(self.corpus, backoff=self.default_tagger)
bitagger = BigramTagger(self.corpus, backoff=unitagger)
tritagger = TrigramTagger(self.corpus, backoff=bitagger)
ntagger = NgramTagger(4, self.corpus, backoff=tritagger)
encoded = self.encoder.encode(ntagger)
decoded = self.decoder.decode(encoded)
self.assertEqual(repr(ntagger), repr(decoded))
self.assertEqual(repr(tritagger), repr(decoded.backoff))
self.assertEqual(repr(bitagger), repr(decoded.backoff.backoff))
self.assertEqual(repr(unitagger), repr(decoded.backoff.backoff.backoff))
self.assertEqual(repr(self.default_tagger),
repr(decoded.backoff.backoff.backoff.backoff))
def train_brill_tagger(tagged_sents):
# The brill tagger module in NLTK.
Template._cleartemplates()
templates = brill24() # or fntbl37
# default_tagger = nltk.DefaultTagger('MORA_HAUPT')
patterns = [(r'''(b|c|d|f|g|h|j|k|l|m|n||p|q|r|s|t|v|w|x|z)e
(b|c|d|f|g|h|j|k|l|m|n||p|q|r|s|t|v|w|x|z)''', 'MORA'),
(r'.*(a|e|i|o|u|ä|î|ô|ü)(a|e|i|o|u|ä|î|ô|ü)', 'DOPPEL'),
(r'.*', 'MORA_HAUPT')] # default
regex_tagger = nltk.RegexpTagger(patterns)
tagger1 = UnigramTagger(tagged_sents, backoff=regex_tagger)
# cutoff = 3, if necessary
tagger2 = BigramTagger(tagged_sents, backoff=tagger1)
tagger3 = TrigramTagger(tagged_sents, backoff=tagger2)
tagger4 = brill_trainer.BrillTaggerTrainer(tagger3, templates, trace=3)
tagger5 = tagger4.train(tagged_sents, max_rules=200)
print
return tagger5
class Tagger:
def __init__(self, idiom):
self.tagger0 = DefaultTagger('N')
self.tagger1 = UnigramTagger(None, self.tagger0)
self.tagger2 = BigramTagger(None, self.tagger1)
self.lang = os.path.abspath('FriggAnswer')+'/pickle/'
#self.lang = os.path.abspath('pickle')+'\\'
self.loadIdiom(idiom)
def loadIdiom(self, idiom):
input = open(self.lang +idiom +'1.pkl', 'rb')
self.tagger = load(input)
input.close()
input = open(self.lang +idiom+'2.pkl', 'rb')
self.tagger2 = load(input)
input.close()
def classify(self, question):
tags = self.tagger2.tag(question)
return tags
def train_tagger():
'''
Um exemplo de treinamento de um etiquetador sintático usando
um modelo de tri-gramas baseado em probabilidades.
Um etiquetador sintático identifica quais a classe de uma palavra
Ex.: Isso é um teste = Isso-PROSUB é-V um-ART teste-N
Preposição Verbo Artigo Substantivo
'''
# Carregando um conjunto de dados em português que possui
# sentenças manualmente identificadas
data = [
[(w, re.split('[|-]', tag)[0]) for w, tag in sent]
for sent in mac_morpho.tagged_sents()]
# Classe sintática padrão. N siginifica Nome/substantivo
tagger0 = DefaultTagger('N')
print('train unigram')
tagger1 = UnigramTagger(data, backoff=tagger0)
print('training bigram')
tagger2 = BigramTagger(data, backoff=tagger1)
print('training trigram')
return TrigramTagger(data, backoff=tagger2)
from flask_cors import CORS
from flask_socketio import SocketIO, emit
from logger import getlogger
import nltk
import sys
import timeit
import urllib.request
import json
import settings
import requests
from nltk.corpus import alpino as alp
from nltk.tag import UnigramTagger, BigramTagger
training_corpus = alp.tagged_sents()
unitagger = UnigramTagger(training_corpus)
bitagger = BigramTagger(training_corpus, backoff=unitagger)
pos_tag = bitagger.tag
logger = getlogger(__name__)
app = Flask(__name__, template_folder='html/templates', static_folder='html/static')
CORS(app)
socketio = SocketIO(app)
app.debug = False
@app.route('/')
def index():
return render_template('test4nl.html')
def query_pixabay(nouns):
if nouns:
from nltk.tag import DefaultTagger, UnigramTagger, BigramTagger, TrigramTagger
from nltk.corpus import treebank
from tag_util import backoff_tagger
train_sents = treebank.tagged_sents()[:3000]
test_sents = treebank.tagged_sents()[3000:]
bitagger = BigramTagger(train_sents)
print(bitagger.evaluate(test_sents))
tritagger = TrigramTagger(train_sents)
print(tritagger.evaluate(test_sents))
default_tagger = DefaultTagger('NN')
combined_tagger = backoff_tagger(train_sents, [UnigramTagger, BigramTagger, TrigramTagger], backoff=default_tagger)
print(combined_tagger.evaluate(test_sents))
# # train
# default_tagger = DefaultTagger('NN')
#
# train_sents = treebank.tagged_sents()[:3000]
# tagger = UnigramTagger(train_sents, backoff=default_tagger)
#
# # test
# test_sents = treebank.tagged_sents()[3000:]
# print(tagger.evaluate(test_sents))
#
# # save to pickle
# import pickle
# with open('unitagger.pkl', 'wb') as output:
# pickle.dump(tagger, output)
# finalise a sequential tagger
# =============================================================================
"""
""" 1. run tagger with different corpus size (50% and 100%) """
# backoff tagger
tag1_eval = dict()
# train with backoff and Brill
tic()
tag1_tagger = DefaultTagger('NO')
tag1_tagger = AffixTagger(train_sents, affix_length=-1, backoff=tag1_tagger)
tag1_tagger = AffixTagger(train_sents, affix_length=-2, backoff=tag1_tagger)
tag1_tagger = AffixTagger(train_sents, affix_length=-3, backoff=tag1_tagger)
tag1_tagger = AffixTagger(train_sents, affix_length=-4, backoff=tag1_tagger)
tag1_tagger = AffixTagger(train_sents, affix_length=-5, backoff=tag1_tagger)
tag1_tagger = UnigramTagger(train_sents, cutoff=3, backoff=tag1_tagger)
tag1_tagger = BigramTagger(train_sents, backoff=tag1_tagger)
tag1_tagger = TrigramTagger(train_sents, backoff=tag1_tagger)
tag1b_tagger = train_brill_tagger(tag1_tagger,
train_sents,
True,
max_rules=100)
tag1_eval['train_time'] = toc()
# test
tic()
tag1_eval['test_accuracy'] = tag1b_tagger.evaluate(val_sents)
tag1_eval['test_time'] = toc()
# display results
display_training_metrics(tag1_eval)
"""
# =============================================================================
# finalise a classification-based tagger
def train_dutch_tagger():
training_corpus = alp.tagged_sents()
unitagger = UnigramTagger(training_corpus)
bitagger = BigramTagger(training_corpus, backoff=unitagger)
pos_tag = bitagger.tag
return pos_tag
default_tagger = nltk.DefaultTagger('NN')
print(default_tagger.evaluate(brown_tagged_sents))
# 0.13089484257215028
brown_tagged_sents2 = [[('The', 'AT'), ('Fulton', 'NP-TL'), ('manner', 'NN')]]
print(default_tagger.evaluate(brown_tagged_sents2))
# 0.3333333333333333
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
brill.Template(brill.Word([-3, -2, -1])),
brill.Template(brill.Word([1, 2, 3])),
brill.Template(brill.Word([-1]), brill.Word([1])),
]
trainer = brill_trainer.BrillTaggerTrainer(initial_tagger, templates, deterministic=True)
return trainer.train(train_sents, **kwargs)
defaultTagger = DefaultTagger('NN')
initialTagger = backoff_tagger(brown_train_sents, [UnigramTagger, BigramTagger, TrigramTagger], backoff=defaultTagger)
brillTagger = train_brill_tagger(initialTagger, brown_train_sents)
tnt_tagger = tnt.TnT(N=100)
tnt_tagger.train(brown_train_sents)
bigramTagger = BigramTagger(brown_train_sents)
trigramTagger = TrigramTagger(brown_train_sents)
print("------------Recommended Tagger------------")
print(nltk.pos_tag(sent))
print("------------Default Tagger------------")
print(defaultTagger.tag(sent))
print("------------Unigram Tagger Overrode------------")
unigramTagger = UnigramTagger(model={'Pierre': 'NN'})
print(unigramTagger.tag(sent))
print("------------Unigram Tagger Trained------------")
unigramTagger = UnigramTagger(brown_train_sents)
print(unigramTagger.tag(sent))
import nltk from nltk.corpus import alpino as alp from nltk.tag import UnigramTagger, BigramTagger alpino = alp.tagged_sents() unitagger = UnigramTagger(alpino) bitagger = BigramTagger(alpino, backoff=unitagger) pos_tag = bitagger.tag sent = 'Een telescoop is een instrument dat een astronoom gebruikt .'.split() print(pos_tag(sent))
from nltk.tag import BigramTagger as BigT from nltk.tag import TrigramTagger as TriT biTagger=BigT(train_sents) biTagger.evaluate(test_sents) triTagger=TriT(train_sents) triTagger.evaluate(test_sents)
(r".*", "NN") # Nouns (default)
]
rt = RegexpTagger(regexps=patterns)
print(rt.evaluate(test_data))
print(rt.tag(tokens))
# 3. N-GRAM TAGGERS:
# Contiguous sequences of n items from a sequence of text or speech. Items can be words, phonemes,
# letters, characters or syllabes. Shingles: n-grams where items are just words.
# UnigramTagger -> NGramTagger -> ContextTagger -> SequentialBackoffTagger
# Train the N-Gram taggers using the training_data (pre-tagged tokens, i.e. labeled observations)
ut = UnigramTagger(train=train_data)
bt = BigramTagger(train_data)
tt = TrigramTagger(train_data)
# Test the performance of each N-Gram tagger
print("1-Gram Tagger Accuracy: {}".format(ut.evaluate(test_data)))
print("2-Gram Tagger Accuracy: {}".format(bt.evaluate(test_data)))
print("3-Gram Tagger Accuracy: {}".format(tt.evaluate(test_data)))
print("\n1-Gram tags:")
print(ut.tag(tokens))
print("\n2-Gram tags:")
print(bt.tag(tokens))
print("\n3-Gram tags:")
print(tt.tag(tokens))
for page in list(root):
l = []
text = page.find('text').text.decode('utf8')
language = page.find('language').text.decode('utf8')
pos = page.find('pos_tags').text.decode('utf8')
splitText = text.split(" ")[1:-1]
posText = pos.split(" ")[1:-1]
for i in range(len(splitText)):
l.append((splitText[i], posText[i]))
data.append(l)
count = count + 1
shuffle(data)
# Divide data into train and test sets
eightyPercent = count*0.9
training_set = data[0:int(eightyPercent)]
test_set = data[int(eightyPercent):]
# Train
train_data = training_set
tag1 = DefaultTagger('NN')
tag2 = UnigramTagger(train_data, backoff = tag1)
tag3 = BigramTagger(train_data, backoff = tag2)
tag4 = TrigramTagger(train_data, backoff = tag3)
# Accuracy
# print tag4.tag('open a start up'.encode('utf-8').decode('utf-8').split())
# print tag4.tag('OUT nahi KARDO ISSE BAHUT HOGAYA aaj Salman'.encode('utf-8').decode('utf-8').split())
gold_sentences = test_set
print tag4.evaluate(gold_sentences)
def train(self, sentence_list):
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)
self.tagger = TrigramTagger(sentence_list, backoff=bigram_fallback)
# Evaluation set
evaulation_data = tagged_data_list[cutoff:development_size]
# print "Data is splitted!"
# Regular expression tagger
nn_cd_tagger = RegexpTagger([(r'^-?[0-9]+(.[0-9]+)?$', 'PUNC'),
(r'.*', 'NOUN_NOM')])
# Unigram tagger
unigram_tagger = UnigramTagger(training_data, backoff=nn_cd_tagger)
print "Unigram accuracy: "
print unigram_tagger.evaluate(evaulation_data)
# Bigram tagger
bigram_tagger = BigramTagger(training_data, backoff=unigram_tagger)
print "Bigram accuracy: "
print bigram_tagger.evaluate(evaulation_data)
# Trigram tagger
trigram_tagger = TrigramTagger(training_data, backoff=bigram_tagger)
print "Trigram accuracy: "
print trigram_tagger.evaluate(evaulation_data)
# Brill tagger templates
templates = [
Template(brill.Pos([1, 1])),
Template(brill.Pos([2, 2])),
Template(brill.Pos([1, 2])),
Template(brill.Pos([1, 3])),
Template(brill.Word([1, 1])),
from nltk.corpus import treebank
from nltk.corpus import wordnet as wn
from os.path import isfile, join
from os import listdir
from pprint import pprint
import gensim.downloader as api
import re
import nltk
import os
TEST_PATH = '../test/untagged'
COMMON_WORDS_PATH = '../resources/1-1000.txt'
TRAINING_SENTS = treebank.tagged_sents()
UNIGRAM = UnigramTagger(TRAINING_SENTS, backoff=DefaultTagger('NN'))
BIGRAM = BigramTagger(TRAINING_SENTS, backoff=UNIGRAM)
TRIGRAM = TrigramTagger(TRAINING_SENTS, backoff=BIGRAM)
STOPWORDS = set(nltk.corpus.stopwords.words('english'))
WORD_VECTORS = api.load("glove-wiki-gigaword-100")
TEST_FILES = [f for f in listdir(TEST_PATH) if isfile(join(TEST_PATH, f))]
# Manual list of words to be considered "irrelevant"
IRRELEVANT_WORDS = ["talk", "seminar", "lecture"]
# manually created ontology tree, which is later extended
TREE = {"science": {}, "maths": {}, "engineering": {}, "medicine": {}}
# code to convert POS tags into the right form for lemmatization
# https://stackoverflow.com/questions/25534214/nltk-wordnet-lemmatizer-shouldnt-it-lemmatize-all-inflections-of-a-word
POS_TO_WORDNET = {
def indivBigram(bambara, backoff):
bigram= BigramTagger(bambara.train_sents, backoff=backoff)
print("Bigram accuracy: ",bigram.evaluate(bambara.test_sents))
return bigram
from nltk.tag import UnigramTagger
unigramTagger = UnigramTagger(training, cutoff=2)
# same as tagger.train(training)
print('Uniigram tagger accuracy:')
print(unigramTagger.evaluate(testing))
#-----------------------------------------------------
print('Bigram tagger accuracy:')
from nltk.tag import BigramTagger
bigramTagger = BigramTagger(training)
print(bigramTagger.evaluate(testing))
#-----------------------------------------------------
print('Trigram tagger accuracy:')
from nltk.tag import TrigramTagger
trigramTagger = TrigramTagger(training)
print(trigramTagger.evaluate(testing))
#-----------------------------------------------------
#Brill Tagger
from nltk.tag import brill, brill_trainer
# make sure you've got some train_sents!
X_train = tagged_sentences[:int(len(tagged_sentences) * 0.8)]
X_test = tagged_sentences[int(len(tagged_sentences) * 0.8):]
'''
Question 2 - Performance of 0.13, 0.9 and 0.91
'''
# using only the default - NN - 0.1308
default_tagger = nltk.DefaultTagger('NN')
print(default_tagger.evaluate(tagged_sentences))
# Unigrams - 0.902
unigram_tagger = UnigramTagger(X_train)
print(unigram_tagger.evaluate(X_test))
# Bigrams with backoff of unigrams - 0.911
bigram_tagger = BigramTagger(X_train, backoff=unigram_tagger)
print(bigram_tagger.evaluate(X_test))
'''
Question 3 Performace of 0.77 and 0.79
'''
treebank_tagged_sents = nltk.corpus.treebank.tagged_sents(tagset='universal')
print(default_tagger.evaluate(treebank_tagged_sents))
print(unigram_tagger.evaluate(treebank_tagged_sents)) # 0.77
print(bigram_tagger.evaluate(treebank_tagged_sents)) # 0.79
'''
Question 4-5 - F1 of 0.972 for brown dataset. Better performance
'''
# modified code
def word2features(sent, i):
# Evaluation set
evaulation_data = tagged_data_list[cutoff:development_size]
# print "Data is splitted!"
# Regular expression tagger
nn_cd_tagger = RegexpTagger([(r'^-?[0-9]+(.[0-9]+)?$', 'PUNC'), (r'.*', 'NOUN_NOM')])
# Unigram tagger
unigram_tagger = UnigramTagger(training_data, backoff=nn_cd_tagger)
print "Unigram accuracy: "
print unigram_tagger.evaluate(evaulation_data)
# Bigram tagger
bigram_tagger = BigramTagger(training_data, backoff=unigram_tagger)
print "Bigram accuracy: "
print bigram_tagger.evaluate(evaulation_data)
# Trigram tagger
trigram_tagger = TrigramTagger(training_data, backoff=bigram_tagger)
print "Trigram accuracy: "
print trigram_tagger.evaluate(evaulation_data)
# Brill tagger templates
templates = [
Template(brill.Pos([1, 1])),
Template(brill.Pos([2, 2])),
Template(brill.Pos([1, 2])),
Template(brill.Pos([1, 3])),
Template(brill.Word([1, 1])),
# 查看特征名
one_hot_multi.classes_ # 查看特征名
from nltk.corpus import brown
from nltk.tag import UnigramTagger
from nltk.tag import BigramTagger
from nltk.tag import TrigramTagger
# 从布朗语料库中获取文本数据，切分为句子
sentences = brown.tagged_sents(categories='news')
# 将4000个句子用作训练，623个句子用作测试
train = sentences[:4000]
test = sentences[4000:]
# 创建回退标注器
unigram = UnigramTagger(train)
bigram = BigramTagger(train, backoff=unigram)
trigram = TrigramTagger(train, backoff=bigram)
# 查看准确率
trigram.evaluate(test)
# TF-IDF
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
# 创建文本
text_data = np.array(
['I love Brazil. Brazil!', 'Sweden is best', 'Germany beats both'])
# 创建TF-IDF特征矩阵
tfidf = TfidfVectorizer()
feature_matrix = tfidf.fit_transform(text_data)
# 查看TF-IDF特征矩阵
feature_matrix
###----------------------------------------------------------------------------
### Train Tagger ( Unitagger )
#Attempt to use the bigram-callout annotation identifier
#If the Bigram callout cannot find the tag, try Unigram the callout
#If the Unigram callout cannot find the tag, use the default callout
from nltk.tag import DefaultTagger
from nltk.tag import UnigramTagger
from nltk.tag import BigramTagger
from nltk.corpus import treebank
print("Preparing, please wait\nLoading...")
train = treebank.tagged_sents()[:7000]
t0 = DefaultTagger('NN')
t1 = UnigramTagger(train, backoff=t0)
t2 = BigramTagger(train, backoff=t1)
### Train Rate_Marker
'''
Sear_Edit_Dis = [None] * 10
Sear_Means1_Right = [None] * 5
Sear_Means1_Wrong = [None] * 5
Sear_Rou_Count = 0
Sear_Line_Count = 0
Sear_Total_Line = 0
Sear_Corre_Line = 0
Sear_Ques = [None] * 10
Sear_Answ = [None] * 10
import nltk from nltk.tag import BigramTagger, TrigramTagger from nltk.corpus import treebank testing = treebank.tagged_sents()[2000:] training= treebank.tagged_sents()[:7000] bigramtag = BigramTagger(training) print(bigramtag.evaluate(testing)) trigramtag = TrigramTagger(training) print(trigramtag.evaluate(testing))
>>>brown_tagged_sents = brown.tagged_sents(categories='news')
>>>default_tagger = nltk.DefaultTagger('NN')
>>>print default_tagger.evaluate(brown_tagged_sents)
# N-gram taggers
>>>from nltk.tag import UnigramTagger
>>>from nltk.tag import DefaultTagger
>>>from nltk.tag import BigramTagger
>>>from nltk.tag import TrigramTagger
# we are dividing the data into a test and train to evaluate our taggers.
>>>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)
>>>bigram_tagger= BigramTagger(train_data, backoff=unigram_tagger)
>>>print bigram_tagger.evaluate(test_data)
>>>trigram_tagger=TrigramTagger(train_data,backoff=bigram_tagger)
>>>print trigram_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'^-?[0-9]+(.[0-9]+)?$', 'CD'), # cardinal numbers
(r'.*', 'NN') # nouns (default) ...
]
rt = RegexpTagger(patterns)
print rt.evaluate(test_data)
print rt.tag(tokens)
## N gram taggers
from nltk.tag import UnigramTagger
from nltk.tag import BigramTagger
from nltk.tag import TrigramTagger
ut = UnigramTagger(train_data)
bt = BigramTagger(train_data)
tt = TrigramTagger(train_data)
print ut.evaluate(test_data)
print ut.tag(tokens)
print bt.evaluate(test_data)
print bt.tag(tokens)
print tt.evaluate(test_data)
print tt.tag(tokens)
def combined_tagger(train_data, taggers, backoff=None):
for tagger in taggers:
backoff = tagger(train_data, backoff=backoff)
return backoff
import nltk from nltk.tag import BigramTagger from nltk.corpus import treebank training_1= treebank.tagged_sents()[:7000] bigramtagger=BigramTagger(training_1) print(treebank.sents()[0]) print(bigramtagger.tag(treebank.sents()[0])) testing_1 = treebank.tagged_sents()[2000:] print(bigramtagger.evaluate(testing_1))
'''import replacer
from replacer import RegexpReplacer
from replacer import RepeatReplacer'''
import linecache
import matplotlib.pyplot as plt
'''
Train Tagger
'''
from nltk.tag import DefaultTagger
from nltk.tag import UnigramTagger
from nltk.tag import BigramTagger
from nltk.corpus import treebank
train = treebank.tagged_sents()[:10000]
t0 = DefaultTagger('NN')
t1 = UnigramTagger(train, backoff=t0)
t2 = BigramTagger(train, backoff=t1)
'''
Initialize
'''
my_corp = web.sents(fileids='firefox.txt')
sent_count = 0
ques_count = 0
All_count = 1
NN_count = 0
NNS_count = 0
NNP_count = 0
VB_count = 0
VBN_count = 0
VBG_count = 0
VBD_count = 0
VBZ_count = 0
def cltk_pos_cv(full_training_set, local_dir_rel):
print("full_training_set", full_training_set)
unigram_accuracies = []
bigram_accuracies = []
trigram_accuracies = []
backoff_accuracies = []
tnt_accuracies = []
with open(full_training_set) as f:
training_set_string = f.read()
pos_set = training_set_string.split('\n\n') # mk into a list
sentence_count = len(pos_set) # 3473
tenth = math.ceil(int(sentence_count) / int(10))
random.seed(0)
random.shuffle(pos_set)
def chunks(l, n):
"""Yield successive n-sized chunks from l.
http://stackoverflow.com/a/312464
"""
for i in range(0, len(l), n):
yield l[i:i+n]
# a list of 10 lists
ten_parts = list(chunks(pos_set, tenth)) # a list of 10 lists with ~347 sentences each
#for counter in list(range(10)):
for counter, part in list(enumerate(ten_parts)):
# map test list to part of given loop
test_set = ten_parts[counter] # or: test_set = part
# filter out this loop's test index
training_set_lists = [x for x in ten_parts if x is not ten_parts[counter]]
# next concatenate the list together into 1 file ( http://stackoverflow.com/a/952952 )
training_set = [item for sublist in training_set_lists for item in sublist]
# save shuffled tests to file (as NLTK trainers expect)
#local_dir_rel = '~/cltk_data/user_data'
local_dir = os.path.expanduser(local_dir_rel)
if not os.path.isdir(local_dir):
os.makedirs(local_dir)
test_path = os.path.join(local_dir, 'test.pos')
with open(test_path, 'w') as f:
f.write('\n\n'.join(test_set))
train_path = os.path.join(local_dir, 'train.pos')
with open(train_path, 'w') as f:
f.write('\n\n'.join(training_set))
# read POS corpora
print("local_dir", local_dir)
train_reader = TaggedCorpusReader(local_dir, 'train.pos')
train_sents = train_reader.tagged_sents()
test_reader = TaggedCorpusReader(local_dir, 'test.pos')
test_sents = test_reader.tagged_sents()
print('Loop #' + str(counter))
# make unigram tagger
unigram_tagger = UnigramTagger(train_sents)
# evaluate unigram tagger
unigram_accuracy = None
unigram_accuracy = unigram_tagger.evaluate(test_sents)
unigram_accuracies.append(unigram_accuracy)
print('Unigram:', unigram_accuracy)
# make bigram tagger
bigram_tagger = BigramTagger(train_sents)
# evaluate bigram tagger
bigram_accuracy = None
bigram_accuracy = bigram_tagger.evaluate(test_sents)
bigram_accuracies.append(bigram_accuracy)
print('Bigram:', bigram_accuracy)
# make trigram tagger
trigram_tagger = TrigramTagger(train_sents)
# evaluate trigram tagger
trigram_accuracy = None
trigram_accuracy = trigram_tagger.evaluate(test_sents)
trigram_accuracies.append(trigram_accuracy)
print('Trigram:', trigram_accuracy)
# make 1, 2, 3-gram backoff tagger
tagger1 = UnigramTagger(train_sents)
tagger2 = BigramTagger(train_sents, backoff=tagger1)
tagger3 = TrigramTagger(train_sents, backoff=tagger2)
# evaluate trigram tagger
backoff_accuracy = None
backoff_accuracy = tagger3.evaluate(test_sents)
backoff_accuracies.append(backoff_accuracy)
print('1, 2, 3-gram backoff:', backoff_accuracy)
# make tnt tagger
tnt_tagger = tnt.TnT()
tnt_tagger.train(train_sents)
# evaulate tnt tagger
tnt_accuracy = None
tnt_accuracy = tnt_tagger.evaluate(test_sents)
tnt_accuracies.append(tnt_accuracy)
print('TnT:', tnt_accuracy)
final_accuracies_list = []
mean_accuracy_unigram = mean(unigram_accuracies)
standard_deviation_unigram = stdev(unigram_accuracies)
uni = {'unigram': {'mean': mean_accuracy_unigram, 'sd': standard_deviation_unigram}}
final_accuracies_list.append(uni)
mean_accuracy_bigram = mean(bigram_accuracies)
standard_deviation_bigram = stdev(bigram_accuracies)
bi = {'bigram': {'mean': mean_accuracy_bigram, 'sd': standard_deviation_bigram}}
final_accuracies_list.append(bi)
mean_accuracy_trigram = mean(trigram_accuracies)
standard_deviation_trigram = stdev(trigram_accuracies)
tri = {'trigram': {'mean': mean_accuracy_trigram, 'sd': standard_deviation_trigram}}
final_accuracies_list.append(tri)
mean_accuracy_backoff = mean(backoff_accuracies)
standard_deviation_backoff = stdev(backoff_accuracies)
back = {'1, 2, 3-gram backoff': {'mean': mean_accuracy_backoff, 'sd': standard_deviation_backoff}}
final_accuracies_list.append(back)
mean_accuracy_tnt = mean(tnt_accuracies)
standard_deviation_tnt = stdev(tnt_accuracies)
tnt_score = {'tnt': {'mean': mean_accuracy_tnt, 'sd': standard_deviation_tnt}}
final_accuracies_list.append(tnt_score)
final_dict = {}
for x in final_accuracies_list:
final_dict.update(x)
return final_dict
