def load_data(self, percentage):
print("Started Loading the Data")
# Get the complete data
data_set = treebank.fileids()
# Partition the data into train and test data sets
training_data_fileIds = [file for file in data_set if "wsj_00" in str(file)]
testing_data_fileIds = [file for file in data_set if "wsj_01" in str(file)]
# How much percentage of files consider for training?
index = int(percentage*len(training_data_fileIds))
training_data_fileIds = training_data_fileIds[:index]
tagged_training_data = treebank.tagged_sents(fileids=training_data_fileIds)
tagged_testing_data = treebank.tagged_sents(fileids=testing_data_fileIds)
tagged_training_words = treebank.tagged_words(fileids=training_data_fileIds)
tagged_testing_words = treebank.tagged_words(fileids=testing_data_fileIds)
# print(len(tagged_training_data1), len(tagged_testing_data1))
# UnTag the data for other uses
untagged_training_data = [untag(item) for item in tagged_training_data]
untagged_testing_data = [untag(item) for item in tagged_testing_data]
print("Data Loaded Successfully. Stats are")
print("Training Data Sentences: ", len(tagged_training_data))
print("Testing Data Sentences: ", len(tagged_testing_data))
return tagged_training_data, tagged_testing_data, tagged_training_words, tagged_testing_words, untagged_training_data, untagged_testing_data
def _untag_sequence(tagged):
try:
if isinstance(tagged[0][0], str):
return tuple(untag(tagged))
else:
return [tuple(untag(t)) for t in tagged]
except IndexError:
return []
def trainUniTnT(self):
"""train unigram and tnt seperatly without DefaultTagger"""
self.split_into_folds()
for k in range(1, (self.folds + 1)):
train_sents = sum(self.foldlist[: (self.folds - 1)], [])
tnt_tagger = tnt.TnT(N=100)
tnt_tagger.train(train_sents)
print(str(k) + " fold: tnt evaluated")
unigram = UnigramTagger(train_sents)
print(str(k) + " fold: unigram evaluated")
to_tag = [untag(i) for i in self.foldlist[self.folds - 1]]
self.tnt_tagged += tnt_tagger.tag_sents(to_tag)
self.uni_tagged += unigram.tag_sents(to_tag)
self.org_tagged += self.foldlist[self.folds - 1]
self.foldlist = [self.foldlist[self.folds - 1]] + self.foldlist[: (self.folds - 1)]
self.tnt = tnt_tagger
self.unigram = unigram
self.tnt_avg_acc = accuracy(sum(self.org_tagged, []), sum(self.tnt_tagged, []))
self.uni_avg_acc = accuracy(sum(self.org_tagged, []), sum(self.uni_tagged, []))
print("Accuracy of concatenated tnt-tagged sentences: ", self.tnt_avg_acc)
print("Accuracy of concatenated unigram-tagged sentences: ", self.uni_avg_acc)
(self.tnt_tagprecision, self.tnt_tagrecall) = self.tagprecision_recall(
tnt_tagger, self.tnt_tagged, self.org_tagged
)
(self.unigram_tagprecision, self.unigram_tagrecall) = self.tagprecision_recall(
unigram, self.uni_tagged, self.org_tagged
)
# delete following values so that trainRegexp has the inicial values
self.org_tagged = []
self.foldlist = []
for i in range(1, self.folds + 1):
self.foldlist.append(self.create_fold(i))
def update_category_by_pos():
from nltk.corpus import brown
from nltk import NaiveBayesClassifier
from nltk import classify
from nltk.tag import untag
from nltk import DecisionTreeClassifier
def pos_features(sentence, i):
features = {'suffix(1)':sentence[i][-1:],
'suffix(2)':sentence[i][-2:],
'suffix(3)':sentence[i][-3:]
}
features['prev-word'] = '<start>' if i==0 else sentence[i-1]
return features
print pos_features(brown.sents()[0], 8)
tagged_sents = brown.tagged_sents(categories='news')
featuresets = []
for tagged_sent in tagged_sents:
untagged_sent = untag(tagged_sent)
for i, (word, tag) in enumerate(tagged_sent):
featuresets.append((pos_features(untagged_sent, i), tag))
size = int(len(featuresets) * 0.1)
train_set, test_set = featuresets[size:], featuresets[:size]
# classifier = NaiveBayesClassifier.train(train_set)
classifier = DecisionTreeClassifier.train(train_set)
print 'NaiveBay %f' % classify.accuracy(classifier, test_set)
def entropy_of_words(self, tagged_data):
"""
Takes in tagged words as input and return tag entropies of the words
"""
tagged_words_fdist = Counter(tagged_data)
total_no_of_tagged_words = sum(tagged_words_fdist.values())
untagged_data = untag(tagged_data)
untagged_words_fdist = Counter(untagged_data)
total_no_of_words = sum(untagged_words_fdist.values())
# Create Word Tags dictionary as shown below in format
word_tags = dict() # {word:{(word,tag1),....(word,tagN)},.....}
for tagged_word in tagged_words_fdist.keys():
if tagged_word[0] in word_tags.keys():
word_tags[tagged_word[0]].add(tagged_word)
else:
word_tags[tagged_word[0]] = set()
word_tags[tagged_word[0]].add(tagged_word)
# Compute the entropies of the words
entropies = dict()
for word in untagged_words_fdist.keys():
entropies[word] = 0
tagged_words = word_tags[word]
for tagged_word in tagged_words:
yi = tagged_words_fdist[tagged_word]/untagged_words_fdist[word]
entropies[word] += -(yi*log2(yi))
entropies = sorted(entropies.items(), key=operator.itemgetter(1), reverse=True)
return entropies, word_tags, tagged_words_fdist
def trainRegexp(self, backoff):
self.split_into_folds()
for k in range(1, (self.folds + 1)):
train_sents = sum(self.foldlist[: (self.folds - 1)], [])
if self.option_tone == "tonal" and self.option_tag == "Affixes":
regex = RegexpTonalSA(backoff)
if self.option_tone == "tonal" and self.option_tag == "POS":
regex = RegexpTonal(backoff)
if self.option_tone == "nontonal" and self.option_tag == "Affixes":
regex = RegexpSA(backoff)
if self.option_tone == "nontonal" and self.option_tag == "POS":
regex = Regexp(backoff)
to_tag = [untag(i) for i in self.foldlist[self.folds - 1]]
self.regex_tagged += regex.tag_sents(to_tag)
self.org_tagged += self.foldlist[self.folds - 1]
self.foldlist = [self.foldlist[self.folds - 1]] + self.foldlist[: (self.folds - 1)]
self.regex = regex
self.regex_avg_acc = accuracy(sum(self.org_tagged, []), sum(self.regex_tagged, []))
print("Accuracy of concatenated regexp-tagged sentences: ", self.regex_avg_acc)
(self.regex_tagprecision, self.regex_tagrecall) = self.tagprecision_recall(
regex, self.regex_tagged, self.org_tagged
)
self.org_tagged = []
self.foldlist = []
for i in range(1, self.folds + 1):
self.foldlist.append(self.create_fold(i))
def demo(self, test_sents):
tagger = CRFTagger(feature_func=self.feature_detector)
tagger.set_model_file(self.modelpath)
for sent in test_sents:
tagged = tagger.tag(untag(sent))
for s in self._to_sentence(tagged):
print(s)
print(tagger.evaluate(test_sents))
def calc_switched_words(bambara, tagger, tagpairs):
'''iterates over the switched tag-pairs to find all the words which are responsible
for these switches'''
untag_testsents = [untag(i) for i in bambara.test_sents]
tagger_tagged_sents = tagger.tag_sents(untag_testsents)
compareTags = list(zip(sum(tagger_tagged_sents,[]), sum(bambara.test_sents,[])))
word_tag_list = sum(bambara.reader.tagged_sents, [])
switch_list = [(i[0],i[1]) for i in tagpairs]
for i in switch_list:
calc_one_switched_word(i[0], i[1], compareTags, word_tag_list)
def test_MLT(self, model, tagged_testing_data):
"""
Takes the model as well as the tagged testing data SENTENCES to tag the words using most likely tag tagger
:param model dictionary to use for most likely tag tagger
:param tagged_testing_data testing data
:return accuracy
"""
untagged_testing_data = [untag(item) for item in tagged_testing_data]
for sent in untagged_testing_data:
for word in untagged_testing_data:
pass
return
def train(cls,train_sents, feature_extractor, classifier_cls,**kwargs):
train_set = []
for tagged_sent in train_sents:
untagged_sent = untag(tagged_sent)
history = []
for i, (word,tag) in enumerate(tagged_sent):
featureset = feature_extractor(untagged_sent,i, history)
train_set.append((featureset,tag))
history.append(tag)
classifier = classifier_cls.train(train_set, **kwargs)
return cls(feature_extractor, classifier)
def train(cls, train_sents, feature_extractor, classifier_cls, **kwargs):
train_set = []
for tagged_sent in train_sents:
untagged_sent = untag(tagged_sent)
history = []
for i, (word, tag) in enumerate(tagged_sent):
featureset = feature_extractor(untagged_sent, i, history)
train_set.append((featureset, tag))
history.append(tag)
classifier = classifier_cls.train(train_set, **kwargs)
return cls(feature_extractor, classifier)
def __init__(self):
boundary = int(len(brown.tagged_sents())*0.8)
train_naive = brown.tagged_sents(simplify_tags=True)[:boundary]
temp_train_data = []
for sentence in train_naive:
untagged_sent = untag(sentence)
history = []
for i, (word, tag) in enumerate(sentence):
temp_train_data.append((self.featextract(untagged_sent,
i,
history),
tag))
history.append(tag)
self.bayes=naivebayes.NaiveBayesClassifier.train(temp_train_data)
def evaluate(self, gold):
"""
Score the accuracy of the tagger against the gold standard.
Strip the tags from the gold standard text, retag it using
the tagger, then compute the accuracy score.
:type gold: list(list(tuple(str, str)))
:param gold: The list of tagged sentences to score the tagger on.
:rtype: float
"""
from nltk.tag import untag
tagged_sents = self.tag_sents(untag(sent) for sent in gold)
gold_tokens = sum(gold, [])
test_tokens = sum(tagged_sents, [])
return self.accuracy(gold_tokens, test_tokens)
def demo():
myTagger = Tagger()
from nltk.tag import untag
# Load the brown corpus.
from nltk.corpus import brown
#brown_train = brown.tagged_sents()[100:]
brown_test = brown.tagged_sents()[:100]
test_sent = untag(brown_test[1])
score = myTagger.evaluate(brown_test)
print "Score: ", score
result = myTagger.tag(test_sent)
print result
print myTagger.tag(['drink', 'some', 'water'])
def read_and_write():
flist = []
path = "C:\\Users\\gsree\\OneDrive\\Desktop\\Book2.xlsx"
wb_obj = openpyxl.load_workbook(path)
sheet_obj = wb_obj.active
m_row = sheet_obj.max_row
for i in range(2, m_row + 1):
cell_obj = sheet_obj.cell(row=i, column=1)
result = summarization(cell_obj.value)
ci = sheet_obj.cell(row=i, column=8)
ci.value = result
tok = word_tokenize(result)
tagged = pos_tag(tok)
nn_vb_tagged = [(word, tag) for word, tag in tagged
if tag in ('NN', 'JJ', 'NNS', 'VBN', 'RB', 'VBG', 'VB')
]
mylist = untag(nn_vb_tagged)
cj = sheet_obj.cell(row=i, column=9)
str = ""
for l in mylist:
str = str + l + ","
cj.value = str
mylist = (list(dict.fromkeys(mylist)))
#print(mylist)
ck = sheet_obj.cell(row=i, column=10)
string_story = []
string_story = (create_user_story(mylist))
#print(string_story)
st = ""
for i in string_story:
st = st + i
ck.value = st
flist.append(string_story)
#print(flist)
wb_obj.save("C:\\Users\\gsree\\OneDrive\\Desktop\\Book2.xlsx")
generate_html(final_user_story(flist))
def load_data(self, data_set):
"""
Loads the given data set. Makes the data set case insensitive.
Remove words that appear less than 5 times.
:return updated data set
"""
print("Started Loading the Data")
tagged_tokens = data_set.tagged_words()
tokens = untag(tagged_tokens)
# Get the list of words that appear less than 5 times in Corpus
print("Get LT5's")
tokens = [token.lower() for token in tokens] # Convert to lower case
freq_dist = FreqDist(tokens) # Compute the freq dist
tokens_lt_5 = [word for word, count in freq_dist.items() if count < 5]
# Delete words less than 5 and make the corpus insensitive
print("Making data case insensitive")
token_range = range(len(tagged_tokens))
indexed_tokens = OrderedDict(zip(token_range,tagged_tokens))
updated_tagged_tokens = OrderedDict()
for tagged_token_id, tagged_token in indexed_tokens.items():
if tagged_token[0].lower() in tokens_lt_5:
del indexed_tokens[tagged_token_id]
else:
temp = list()
temp.append(tagged_token[0].lower())
temp.append(tagged_token[1])
temp = tuple(temp)
updated_tagged_tokens[tagged_token_id] = temp
tagged_tokens = list(updated_tagged_tokens.values())
# Pickle the data for future purpose
print("Pickling the Updated Corpus")
if data_set == brown:
file_name = "q5_brown_updated.pkl"
else:
file_name = "q5_treebank_updated.pkl"
pkl.dump((tagged_tokens, tokens_lt_5), open(file_name,'wb'))
return tagged_tokens, tokens_lt_5
def get_statistics_per_tag(corpus_test, tagger):
from nltk.tag import untag
possible_tags = get_all_tags(corpus_test)
untaged_test = [untag(x) for x in corpus_test]
tagged_sents = tagger.tag_sents(untaged_test)
ref_words = sum(corpus_test, [])
test_words = sum(tagged_sents, [])
best_fmeasure = 0
best_tag = "Chtulhu"
worst_tag_fmeasure = 100
worst_tag = "cthulhu"
for tag in possible_tags:
f_measure = evaluate_tag(ref_words, test_words, tag)
if f_measure > best_fmeasure:
best_tag = tag
best_fmeasure = f_measure
if f_measure < worst_tag_fmeasure:
worst_tag = tag
worst_tag_fmeasure = f_measure
print "best tag: ", best_tag
print "worst tag: ", worst_tag
def calculate_contingenz_with_sets(self, tagger):
"""
Compares the original tags with the tags created by the tagger.
"""
tagger_tagged = tagger.tag_sents([untag(i) for i in self.test_sents])
tagger_words = sum(tagger_tagged,[])
original_tagged = self.test_sents
original_words = sum(original_tagged,[])
tagged_org_zip = zip([i[1] for i in original_words],[i[1] for i in tagger_words])
contingenzliste = []
orig_tags = []
tag_tags = []
for i in tagged_org_zip:
if i[0] != i[1]:
if i[1] == None:
i = (i[0], "None")
contingenzliste.append(i[1]+" : "+i[0]+"\n")
orig_tags.append(i[0])
tag_tags.append(i[1])
self.contingenzliste = self.contingenzliste + contingenzliste
self.reference_tags = self.reference_tags + orig_tags
self.test_tags = self.test_tags + tag_tags
print "unique words that are None: ", len(self.distinct_nones)
print "precentage of none tokesns to overall tokes: ", float(self.overall_nones) / float(
self.overall_tokens_tagged)
print "precentage of unique nones to overall unique ", float(len(self.distinct_nones)) / float(
self.get_overall_distinct())
if __name__ == "__main__":
# split the brown corpus to test, dev, and test set
all_words = corpus.brown.tagged_sents(tagset='universal')
ds_length = len(all_words)
train = all_words[int(0.2 * ds_length):]
dev = all_words[:int(0.1 * ds_length)]
test = all_words[int(0.1 * ds_length):int(0.2 * ds_length)]
untagged_dev = [untag(item) for item in dev]
words_in_dev = 0
for item in untagged_dev:
words_in_dev += len(item)
print "overall words in dev : ", words_in_dev
u1 = SimpleUnigramTagger(train)
tagged_dev = u1.tag_sents(untagged_dev)
print len(tagged_dev)
none_count = 0
for sent in tagged_dev:
for tagged_word in sent:
if tagged_word[1] is None:
none_count += 1
print "Number of Nones in dev is: ", none_count
print "number of options per (token, word) is:"
# every tagger has a tag() method.
# DefaultTagger is a subclass of SequentialBackoffTagger which has a choose_tag() method.
from nltk.tag import DefaultTagger
from nltk.corpus import treebank
tagger = DefaultTagger('NN')
print(tagger.tag(['Hello', 'World']))
# thought it's too simple, we can try to evaluate it
test_sents = treebank.tagged_sents()[3000:]
print(tagger.evaluate(test_sents))
# for sentences
print(tagger.tag_sents([['Hello', 'World', '.'], ['How', 'are', 'you', '?']]))
# untagging
from nltk.tag import untag
print(untag([('Hello', 'NN'), ('World', 'NN')]))
def train(self, train_sents, max_rules=200, min_score=2, min_acc=None):
"""
Trains the Brill tagger on the corpus *train_sents*,
producing at most *max_rules* transformations, each of which
reduces the net number of errors in the corpus by at least
*min_score*, and each of which has accuracy not lower than
*min_acc*.
#imports
>>> from nltk.tbl.template import Template
>>> from nltk.tag.brill import Pos, Word
>>> from nltk.tag import untag, RegexpTagger, BrillTaggerTrainer
#some data
>>> from nltk.corpus import treebank
>>> training_data = treebank.tagged_sents()[:100]
>>> baseline_data = treebank.tagged_sents()[100:200]
>>> gold_data = treebank.tagged_sents()[200:300]
>>> testing_data = [untag(s) for s in gold_data]
>>> backoff = 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)
... ])
>>> baseline = backoff #see NOTE1
>>> baseline.evaluate(gold_data) #doctest: +ELLIPSIS
0.2450142...
#templates
>>> Template._cleartemplates() #clear any templates created in earlier tests
>>> templates = [Template(Pos([-1])), Template(Pos([-1]), Word([0]))]
#construct a BrillTaggerTrainer
>>> tt = BrillTaggerTrainer(baseline, templates, trace=3)
>>> tagger1 = tt.train(training_data, max_rules=10)
TBL train (fast) (seqs: 100; tokens: 2417; tpls: 2; min score: 2; min acc: None)
Finding initial useful rules...
Found 845 useful rules.
<BLANKLINE>
B |
S F r O | Score = Fixed - Broken
c i o t | R Fixed = num tags changed incorrect -> correct
o x k h | u Broken = num tags changed correct -> incorrect
r e e e | l Other = num tags changed incorrect -> incorrect
e d n r | e
------------------+-------------------------------------------------------
132 132 0 0 | AT->DT if Pos:NN@[-1]
85 85 0 0 | NN->, if Pos:NN@[-1] & Word:,@[0]
69 69 0 0 | NN->. if Pos:NN@[-1] & Word:.@[0]
51 51 0 0 | NN->IN if Pos:NN@[-1] & Word:of@[0]
47 63 16 161 | NN->IN if Pos:NNS@[-1]
33 33 0 0 | NN->TO if Pos:NN@[-1] & Word:to@[0]
26 26 0 0 | IN->. if Pos:NNS@[-1] & Word:.@[0]
24 24 0 0 | IN->, if Pos:NNS@[-1] & Word:,@[0]
22 27 5 24 | NN->-NONE- if Pos:VBD@[-1]
17 17 0 0 | NN->CC if Pos:NN@[-1] & Word:and@[0]
>>> tagger1.rules()[1:3]
(Rule('001', 'NN', ',', [(Pos([-1]),'NN'), (Word([0]),',')]), Rule('001', 'NN', '.', [(Pos([-1]),'NN'), (Word([0]),'.')]))
>>> train_stats = tagger1.train_stats()
>>> [train_stats[stat] for stat in ['initialerrors', 'finalerrors', 'rulescores']]
[1775, 1269, [132, 85, 69, 51, 47, 33, 26, 24, 22, 17]]
>>> tagger1.print_template_statistics(printunused=False)
TEMPLATE STATISTICS (TRAIN) 2 templates, 10 rules)
TRAIN ( 2417 tokens) initial 1775 0.2656 final: 1269 0.4750
#ID | Score (train) | #Rules | Template
--------------------------------------------
001 | 305 0.603 | 7 0.700 | Template(Pos([-1]),Word([0]))
000 | 201 0.397 | 3 0.300 | Template(Pos([-1]))
<BLANKLINE>
<BLANKLINE>
>>> tagger1.evaluate(gold_data) # doctest: +ELLIPSIS
0.43996...
>>> tagged, test_stats = tagger1.batch_tag_incremental(testing_data, gold_data)
>>> tagged[33][12:] == [('foreign', 'IN'), ('debt', 'NN'), ('of', 'IN'), ('$', 'NN'), ('64', 'CD'),
... ('billion', 'NN'), ('*U*', 'NN'), ('--', 'NN'), ('the', 'DT'), ('third-highest', 'NN'), ('in', 'NN'),
... ('the', 'DT'), ('developing', 'VBG'), ('world', 'NN'), ('.', '.')]
True
>>> [test_stats[stat] for stat in ['initialerrors', 'finalerrors', 'rulescores']]
[1855, 1376, [100, 85, 67, 58, 27, 36, 27, 16, 31, 32]]
# a high-accuracy tagger
>>> tagger2 = tt.train(training_data, max_rules=10, min_acc=0.99)
TBL train (fast) (seqs: 100; tokens: 2417; tpls: 2; min score: 2; min acc: 0.99)
Finding initial useful rules...
Found 845 useful rules.
<BLANKLINE>
B |
S F r O | Score = Fixed - Broken
c i o t | R Fixed = num tags changed incorrect -> correct
o x k h | u Broken = num tags changed correct -> incorrect
r e e e | l Other = num tags changed incorrect -> incorrect
e d n r | e
------------------+-------------------------------------------------------
132 132 0 0 | AT->DT if Pos:NN@[-1]
85 85 0 0 | NN->, if Pos:NN@[-1] & Word:,@[0]
69 69 0 0 | NN->. if Pos:NN@[-1] & Word:.@[0]
51 51 0 0 | NN->IN if Pos:NN@[-1] & Word:of@[0]
36 36 0 0 | NN->TO if Pos:NN@[-1] & Word:to@[0]
26 26 0 0 | NN->. if Pos:NNS@[-1] & Word:.@[0]
24 24 0 0 | NN->, if Pos:NNS@[-1] & Word:,@[0]
19 19 0 6 | NN->VB if Pos:TO@[-1]
18 18 0 0 | CD->-NONE- if Pos:NN@[-1] & Word:0@[0]
18 18 0 0 | NN->CC if Pos:NN@[-1] & Word:and@[0]
>>> tagger2.evaluate(gold_data) # doctest: +ELLIPSIS
0.44159544...
>>> tagger2.rules()[2:4]
(Rule('001', 'NN', '.', [(Pos([-1]),'NN'), (Word([0]),'.')]), Rule('001', 'NN', 'IN', [(Pos([-1]),'NN'), (Word([0]),'of')]))
# NOTE1: (!!FIXME) A far better baseline uses nltk.tag.UnigramTagger,
# with a RegexpTagger only as backoff. For instance,
# >>> baseline = UnigramTagger(baseline_data, backoff=backoff)
# However, as of Nov 2013, nltk.tag.UnigramTagger does not yield consistent results
# between python versions. The simplistic backoff above is a workaround to make doctests
# get consistent input.
:param train_sents: training data
:type train_sents: list(list(tuple))
:param max_rules: output at most max_rules rules
:type max_rules: int
:param min_score: stop training when no rules better than min_score can be found
:type min_score: int
:param min_acc: discard any rule with lower accuracy than min_acc
:type min_acc: float or None
:return: the learned tagger
:rtype: BrillTagger
"""
# FIXME: several tests are a bit too dependent on tracing format
# FIXME: tests in trainer.fast and trainer.brillorig are exact duplicates
# Basic idea: Keep track of the rules that apply at each position.
# And keep track of the positions to which each rule applies.
# Create a new copy of the training corpus, and run the
# initial tagger on it. We will progressively update this
# test corpus to look more like the training corpus.
test_sents = [list(self._initial_tagger.tag(untag(sent)))
for sent in train_sents]
# Collect some statistics on the training process
trainstats = {}
trainstats['min_acc'] = min_acc
trainstats['min_score'] = min_score
trainstats['tokencount'] = sum(len(t) for t in test_sents)
trainstats['sequencecount'] = len(test_sents)
trainstats['templatecount'] = len(self._templates)
trainstats['rulescores'] = []
trainstats['initialerrors'] = sum(
tag[1] != truth[1]
for paired in zip(test_sents, train_sents)
for (tag, truth) in zip(*paired)
)
trainstats['initialacc'] = 1 - trainstats['initialerrors']/trainstats['tokencount']
if self._trace > 0:
print("TBL train (fast) (seqs: {sequencecount}; tokens: {tokencount}; "
"tpls: {templatecount}; min score: {min_score}; min acc: {min_acc})".format(**trainstats))
# Initialize our mappings. This will find any errors made
# by the initial tagger, and use those to generate repair
# rules, which are added to the rule mappings.
if self._trace:
print("Finding initial useful rules...")
self._init_mappings(test_sents, train_sents)
if self._trace:
print((" Found %d useful rules." % len(self._rule_scores)))
# Let the user know what we're up to.
if self._trace > 2:
self._trace_header()
elif self._trace == 1:
print("Selecting rules...")
# Repeatedly select the best rule, and add it to `rules`.
rules = []
try:
while (len(rules) < max_rules):
# Find the best rule, and add it to our rule list.
rule = self._best_rule(train_sents, test_sents, min_score, min_acc)
if rule:
rules.append(rule)
score = self._rule_scores[rule]
trainstats['rulescores'].append(score)
else:
break # No more good rules left!
# Report the rule that we found.
if self._trace > 1:
self._trace_rule(rule)
# Apply the new rule at the relevant sites
self._apply_rule(rule, test_sents)
# Update _tag_positions[rule.original_tag] and
# _tag_positions[rule.replacement_tag] for the affected
# positions (i.e., self._positions_by_rule[rule]).
self._update_tag_positions(rule)
# Update rules that were affected by the change.
self._update_rules(rule, train_sents, test_sents)
# The user can cancel training manually:
except KeyboardInterrupt:
print("Training stopped manually -- %d rules found" % len(rules))
# Discard our tag position mapping & rule mappings.
self._clean()
trainstats['finalerrors'] = trainstats['initialerrors'] - sum(trainstats['rulescores'])
trainstats['finalacc'] = 1 - trainstats['finalerrors']/trainstats['tokencount']
# Create and return a tagger from the rules we found.
return BrillTagger(self._initial_tagger, rules, trainstats)
from nltk.tag import pos_tag, untag
from nltk.tokenize import word_tokenize
sentence = 'Emma refused to permit us to obtain the refuse permit"'
# 문장을 단어로 분리하고 그것을 품사와 같이 표시
tagged_list = pos_tag(word_tokenize(sentence))
print(tagged_list)
# t[0] t[1] t[0] t[1] t[0] t[1]
#[('Emma', 'NNP'), ('refused', 'VBD'), ('to', 'TO'), ('permit', 'VB'), ('us', 'PRP'), ('to', 'TO'), ('obtain', 'VB'), ('the', 'DT'), ('refuse', 'NN'), ('permit', 'NN'), ("''", "''")]
noun_list = [t[0] for t in tagged_list if t[1] == 'NN']
# 리스트중 =='NN' 인 명사만 가져온다.
print(noun_list)
# 품사 tag 를 제거 한다.
print(untag(tagged_list))
# 단어와 품사를 묶어서 한 문자열로 변경
def tokenizer(doc):
return ['/'.join(p) for p in tagged_list] # 배열을 하나의 문자열로 '/'로 구분해서 합친다.
print(tokenizer(tagged_list))
# ['Emma/NNP', 'refused/VBD', 'to/TO', 'permit/VB', 'us/PRP', 'to/TO', 'obtain/VB', 'the/DT', 'refuse/NN', 'permit/NN', "''/''"]
def FeatureExtractor(tree):
rc = []
for subtree in tree.subtrees():
if subtree.label() == 'NP':
rc.append(str(untag(subtree)))
return rc
print("wrong input")
last = input("Unigram or Bigram+Affix+Dictionary+Regexp+Default? UNIGRAM/BIGRAM-> ")
print("\nCalculating Brill & Wu Complementarity\n\nAfter the programme finished, please look for \
'Results\CompareBrillWu_"+last+tone+tag+".txt' and 'Results\Disagreement_BrillWuHtml_"+last+tone+tag+".txt' in your current working directory.\n")
bambara = create_reader(tone, tag)
if last == "BIGRAM":
lasttagger = backoff_tagger([5,6,8,1],bambara,option_tones=tone, option_tag=tag)
lasttaggertagged = lasttagger.tag_sents([untag(i) for i in bambara.test_sents])
lasttagger_acc = lasttagger.evaluate(bambara.test_sents)
lasttagger_err = round((1-(lasttagger_acc)),4)
if last == "UNIGRAM":
lasttagger = indivUnigram(bambara, backoff)
lasttaggertagged = lasttagger.tag_sents([untag(i) for i in bambara.test_sents])
lasttagger_acc = lasttagger.evaluate(bambara.test_sents)
lasttagger_err = round((1-(lasttagger_acc)),4)
hmm = indivHMM(bambara)
hmmtagged = hmm.tag_sents([untag(i) for i in bambara.test_sents])
hmm_acc = hmm.evaluate(bambara.test_sents)
hmm_err = round((1 - (hmm_acc)),4)
crf = indivCRF(bambara, tone, tag)
crftagged = crf.tag_sents([untag(i) for i in bambara.test_sents])
def retag_trees(trees, sents):
tagged_sents = tagger.tag_sents([untag(sent) for sent in sents])
for tree, sentence in zip(trees, tagged_sents):
for (n, word) in zip(tree.treepositions('leaves'), sentence):
tree[n] = word
def NounExtractor(tree):
rc1 = []
for subtree in tree.subtrees():
if subtree.label() == 'Noun':
rc1.append(str(untag(subtree)))
return rc1
import nltk
nltk.download('brown')
from nltk.corpus import brown
brown_news_tagged = brown.tagged_sents(categories='news', tagset='universal')
brown_news_words = brown.tagged_words(categories='news', tagset='universal')
brown_train = brown_news_tagged[100:]
brown_test = brown_news_tagged[:100]
from nltk.tag import untag
test_sent = untag(brown_test[0])
print("Tagged: ", brown_test[0])
print()
print("Untagged: ", test_sent)
# A default tagger assigns the same tag to all words
from nltk import DefaultTagger
default_tagger = DefaultTagger('NOUN')
default_tagger.tag('This is a test'.split())
def apply_tagger(tagger, corpus):
return [tagger.tag(untag(sent)) for sent in corpus]
[lm.lemmatize(w) for w in words]
lm.lemmatize("dying", pos="v")
nltk.help.upenn_tagset('VB')
from nltk.tag import pos_tag
sentence = "Emma refused to permit us to obtain the refuse permit"
tagged_list = pos_tag(word_tokenize(sentence))
tagged_list
nouns_list = [t[0] for t in tagged_list if t[1] == "NN"]
nouns_list
from nltk.tag import untag
untag(tagged_list)
def tokenizer(doc):
return ["/".join(p) for p in tagged_list]
tokenizer(sentence)
from nltk import Text
text = Text(retokenize.tokenize(emma_raw), name="Emma")
text.plot(20)
plt.show()
text.dispersion_plot(["Emma", "Knightley", "Frank", "Jane", "Harriet", "Robert"])
tag = input("POS/Affixes? -> ")
backoff = input("DefaultTagger as backoff? J/N-> ")
if backoff =="J":
backoff = DefaultTagger('n')
else:
backoff = None
print("\nCalculating Brill & Wu Complementarity\n\nAfter the programme finished, please look for \
'Results\CompareBrillWu_Regex"+tone+tag+".txt' and 'Results\Disagreement_BrillWuHtml_Regex"+tone+tag+".txt' in your current working directory.\n")
bambara = create_reader(tone, tag)
hmm = indivHMM(bambara)
hmmtagged = hmm.tag_sents([untag(i) for i in bambara.test_sents])
hmm_acc = hmm.evaluate(bambara.test_sents)
hmm_err = round((1 - (hmm_acc)),4)
crf = indivCRF(bambara, tone, tag)
crftagged = crf.tag_sents([untag(i) for i in bambara.test_sents])
crf_acc = crf.evaluate(bambara.test_sents)
crf_err = round((1-(crf_acc)),4)
tnt = indivTnT(bambara, backoff)
tnttagged = tnt.tag_sents([untag(i) for i in bambara.test_sents])
tnt_acc = tnt.evaluate(bambara.test_sents)
tnt_err = round((1-(tnt_acc)),4)
unigram = indivUnigram(bambara, backoff)
unitagged = unigram.tag_sents([untag(i) for i in bambara.test_sents])
# print affix_ugram_backoff.evaluate(test)
# print unigram_affix_backoff.evaluate(test)
# cutoffs = [x*0.1 for x in range(20)]
# for c in cutoffs:
# tagger = EntropyVotingTagger(taggers, c)
# print "Accuracy of entropy voting = ", tagger.evaluate(test)
affix_tagger = EntropyAffixTagger(train)
unigram_tagger = EntropyUnigramTagger(train)
taggers = [unigram_tagger, affix_tagger]
tagger = EntropyVotingTagger(taggers, max_entropy=80)
from nltk.tag import untag
untagged_test = [untag(x) for x in dev]
tagged_sents_uni_affix = unigram_affix_backoff.tag_sents(untagged_test)
tagged_sents_entr = tagger.tag_sents(untagged_test)
affix_mistake = 0
unigram_mistake = 0
overall_mistakes = 0
print "len of dev: ", len(dev)
for tagged_reference_sent, tagged_uni_affix_sent, tagged_entropy_sent in izip(dev, tagged_sents_uni_affix,
tagged_sents_entr):
# import pdb;pdb.set_trace()
for tagged_reference, tagged_uni_affix, tagged_entropy in izip(tagged_reference_sent, tagged_uni_affix_sent,
tagged_entropy_sent):
if tagged_uni_affix[1] != tagged_entropy[1]:
overall_mistakes += 1
print "WE GOT MATCH!"
def VerbExtractor(tree):
rc2 = []
for subtree in tree.subtrees():
if subtree.label() == 'Verb':
rc2.append(str(untag(subtree)))
return rc2
def AdjExtractor(tree):
rc3 = []
for subtree in tree.subtrees():
if subtree.label() == 'Adj':
rc3.append(str(untag(subtree)))
return rc3
import nltk
from nltk.tag import untag
print(untag([('beautiful', 'NN'), ('morning', 'NN')]))
def PhraseExtractor(tree):
rc4 = []
for subtree in tree.subtrees():
if subtree.label() == 'Phrase':
rc4.append(str(untag(subtree)))
return rc4
def PosExtractor(tree):
ps = []
for subtree in tree.subtrees():
if subtree.label() == 'PWD':
ps.append(str(untag(subtree)))
return ps
def train(self, train_sents, max_rules=200, min_score=2, min_acc=None):
"""
Trains the Brill tagger on the corpus *train_sents*,
producing at most *max_rules* transformations, each of which
reduces the net number of errors in the corpus by at least
*min_score*, and each of which has accuracy not lower than
*min_acc*.
#imports
>>> from nltk.tbl.template import Template
>>> from nltk.tag.brill import Pos, Word
>>> from nltk.tag import untag, RegexpTagger, BrillTaggerTrainer
#some data
>>> from nltk.corpus import treebank
>>> training_data = treebank.tagged_sents()[:100]
>>> baseline_data = treebank.tagged_sents()[100:200]
>>> gold_data = treebank.tagged_sents()[200:300]
>>> testing_data = [untag(s) for s in gold_data]
>>> backoff = 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)
... ])
>>> baseline = backoff #see NOTE1
>>> baseline.evaluate(gold_data) #doctest: +ELLIPSIS
0.2450142...
#templates
>>> Template._cleartemplates() #clear any templates created in earlier tests
>>> templates = [Template(Pos([-1])), Template(Pos([-1]), Word([0]))]
#construct a BrillTaggerTrainer
>>> tt = BrillTaggerTrainer(baseline, templates, trace=3)
>>> tagger1 = tt.train(training_data, max_rules=10)
TBL train (fast) (seqs: 100; tokens: 2417; tpls: 2; min score: 2; min acc: None)
Finding initial useful rules...
Found 845 useful rules.
<BLANKLINE>
B |
S F r O | Score = Fixed - Broken
c i o t | R Fixed = num tags changed incorrect -> correct
o x k h | u Broken = num tags changed correct -> incorrect
r e e e | l Other = num tags changed incorrect -> incorrect
e d n r | e
------------------+-------------------------------------------------------
132 132 0 0 | AT->DT if Pos:NN@[-1]
85 85 0 0 | NN->, if Pos:NN@[-1] & Word:,@[0]
69 69 0 0 | NN->. if Pos:NN@[-1] & Word:.@[0]
51 51 0 0 | NN->IN if Pos:NN@[-1] & Word:of@[0]
47 63 16 161 | NN->IN if Pos:NNS@[-1]
33 33 0 0 | NN->TO if Pos:NN@[-1] & Word:to@[0]
26 26 0 0 | IN->. if Pos:NNS@[-1] & Word:.@[0]
24 24 0 0 | IN->, if Pos:NNS@[-1] & Word:,@[0]
22 27 5 24 | NN->-NONE- if Pos:VBD@[-1]
17 17 0 0 | NN->CC if Pos:NN@[-1] & Word:and@[0]
>>> tagger1.rules()[1:3]
(Rule('001', 'NN', ',', [(Pos([-1]),'NN'), (Word([0]),',')]), Rule('001', 'NN', '.', [(Pos([-1]),'NN'), (Word([0]),'.')]))
>>> train_stats = tagger1.train_stats()
>>> [train_stats[stat] for stat in ['initialerrors', 'finalerrors', 'rulescores']]
[1775, 1269, [132, 85, 69, 51, 47, 33, 26, 24, 22, 17]]
>>> tagger1.print_template_statistics(printunused=False)
TEMPLATE STATISTICS (TRAIN) 2 templates, 10 rules)
TRAIN ( 2417 tokens) initial 1775 0.2656 final: 1269 0.4750
#ID | Score (train) | #Rules | Template
--------------------------------------------
001 | 305 0.603 | 7 0.700 | Template(Pos([-1]),Word([0]))
000 | 201 0.397 | 3 0.300 | Template(Pos([-1]))
<BLANKLINE>
<BLANKLINE>
>>> tagger1.evaluate(gold_data) # doctest: +ELLIPSIS
0.43996...
>>> tagged, test_stats = tagger1.batch_tag_incremental(testing_data, gold_data)
>>> tagged[33][12:] == [('foreign', 'IN'), ('debt', 'NN'), ('of', 'IN'), ('$', 'NN'), ('64', 'CD'),
... ('billion', 'NN'), ('*U*', 'NN'), ('--', 'NN'), ('the', 'DT'), ('third-highest', 'NN'), ('in', 'NN'),
... ('the', 'DT'), ('developing', 'VBG'), ('world', 'NN'), ('.', '.')]
True
>>> [test_stats[stat] for stat in ['initialerrors', 'finalerrors', 'rulescores']]
[1855, 1376, [100, 85, 67, 58, 27, 36, 27, 16, 31, 32]]
# a high-accuracy tagger
>>> tagger2 = tt.train(training_data, max_rules=10, min_acc=0.99)
TBL train (fast) (seqs: 100; tokens: 2417; tpls: 2; min score: 2; min acc: 0.99)
Finding initial useful rules...
Found 845 useful rules.
<BLANKLINE>
B |
S F r O | Score = Fixed - Broken
c i o t | R Fixed = num tags changed incorrect -> correct
o x k h | u Broken = num tags changed correct -> incorrect
r e e e | l Other = num tags changed incorrect -> incorrect
e d n r | e
------------------+-------------------------------------------------------
132 132 0 0 | AT->DT if Pos:NN@[-1]
85 85 0 0 | NN->, if Pos:NN@[-1] & Word:,@[0]
69 69 0 0 | NN->. if Pos:NN@[-1] & Word:.@[0]
51 51 0 0 | NN->IN if Pos:NN@[-1] & Word:of@[0]
36 36 0 0 | NN->TO if Pos:NN@[-1] & Word:to@[0]
26 26 0 0 | NN->. if Pos:NNS@[-1] & Word:.@[0]
24 24 0 0 | NN->, if Pos:NNS@[-1] & Word:,@[0]
19 19 0 6 | NN->VB if Pos:TO@[-1]
18 18 0 0 | CD->-NONE- if Pos:NN@[-1] & Word:0@[0]
18 18 0 0 | NN->CC if Pos:NN@[-1] & Word:and@[0]
>>> tagger2.evaluate(gold_data) # doctest: +ELLIPSIS
0.44159544...
>>> tagger2.rules()[2:4]
(Rule('001', 'NN', '.', [(Pos([-1]),'NN'), (Word([0]),'.')]), Rule('001', 'NN', 'IN', [(Pos([-1]),'NN'), (Word([0]),'of')]))
# NOTE1: (!!FIXME) A far better baseline uses nltk.tag.UnigramTagger,
# with a RegexpTagger only as backoff. For instance,
# >>> baseline = UnigramTagger(baseline_data, backoff=backoff)
# However, as of Nov 2013, nltk.tag.UnigramTagger does not yield consistent results
# between python versions. The simplistic backoff above is a workaround to make doctests
# get consistent input.
:param train_sents: training data
:type train_sents: list(list(tuple))
:param max_rules: output at most max_rules rules
:type max_rules: int
:param min_score: stop training when no rules better than min_score can be found
:type min_score: int
:param min_acc: discard any rule with lower accuracy than min_acc
:type min_acc: float or None
:return: the learned tagger
:rtype: BrillTagger
"""
# FIXME: several tests are a bit too dependent on tracing format
# FIXME: tests in trainer.fast and trainer.brillorig are exact duplicates
# Basic idea: Keep track of the rules that apply at each position.
# And keep track of the positions to which each rule applies.
# Create a new copy of the training corpus, and run the
# initial tagger on it. We will progressively update this
# test corpus to look more like the training corpus.
test_sents = [
list(self._initial_tagger.tag(untag(sent))) for sent in train_sents
]
# Collect some statistics on the training process
trainstats = {}
trainstats['min_acc'] = min_acc
trainstats['min_score'] = min_score
trainstats['tokencount'] = sum(len(t) for t in test_sents)
trainstats['sequencecount'] = len(test_sents)
trainstats['templatecount'] = len(self._templates)
trainstats['rulescores'] = []
trainstats['initialerrors'] = sum(
tag[1] != truth[1] for paired in zip(test_sents, train_sents)
for (tag, truth) in zip(*paired))
trainstats['initialacc'] = 1 - trainstats[
'initialerrors'] / trainstats['tokencount']
if self._trace > 0:
print(
"TBL train (fast) (seqs: {sequencecount}; tokens: {tokencount}; "
"tpls: {templatecount}; min score: {min_score}; min acc: {min_acc})"
.format(**trainstats))
# Initialize our mappings. This will find any errors made
# by the initial tagger, and use those to generate repair
# rules, which are added to the rule mappings.
if self._trace:
print("Finding initial useful rules...")
self._init_mappings(test_sents, train_sents)
if self._trace:
print((" Found %d useful rules." % len(self._rule_scores)))
# Let the user know what we're up to.
if self._trace > 2:
self._trace_header()
elif self._trace == 1:
print("Selecting rules...")
# Repeatedly select the best rule, and add it to `rules`.
rules = []
try:
while (len(rules) < max_rules):
# Find the best rule, and add it to our rule list.
rule = self._best_rule(train_sents, test_sents, min_score,
min_acc)
if rule:
rules.append(rule)
score = self._rule_scores[rule]
trainstats['rulescores'].append(score)
else:
break # No more good rules left!
# Report the rule that we found.
if self._trace > 1:
self._trace_rule(rule)
# Apply the new rule at the relevant sites
self._apply_rule(rule, test_sents)
# Update _tag_positions[rule.original_tag] and
# _tag_positions[rule.replacement_tag] for the affected
# positions (i.e., self._positions_by_rule[rule]).
self._update_tag_positions(rule)
# Update rules that were affected by the change.
self._update_rules(rule, train_sents, test_sents)
# The user can cancel training manually:
except KeyboardInterrupt:
print("Training stopped manually -- %d rules found" % len(rules))
# Discard our tag position mapping & rule mappings.
self._clean()
trainstats['finalerrors'] = trainstats['initialerrors'] - sum(
trainstats['rulescores'])
trainstats['finalacc'] = 1 - trainstats['finalerrors'] / trainstats[
'tokencount']
# Create and return a tagger from the rules we found.
return BrillTagger(self._initial_tagger, rules, trainstats)
def NegExtractor(tree):
ns = []
for subtree in tree.subtrees():
if subtree.label() == 'NWD':
ns.append(str(untag(subtree)))
return ns
######### DEFAULT TAGGER ###############
#Assigning the default Tag
from nltk.tag import DefaultTagger, untag
tagger=DefaultTagger('NN')
tokens=[['Hello','World'],['How','are','you','?']]
print tagger.tag(tokens)
print tagger.tag_sents(tokens)
#Untagging
tagged=tagger.tag(tokens)
print untag(tagged)
#Evaluating the tagger accuracy
from nltk.corpus import treebank
test_sents=treebank.tagged_sents()[3000:]
print tagger.evaluate(test_sents)
def trainALL(self, last):
self.split_into_folds()
for k in range(1, (self.folds + 1)):
train_sents = sum(self.foldlist[: (self.folds - 1)], [])
crf = CRFTagger(training_opt={"max_iterations": 100, "max_linesearch": 10, "c1": 0.0001, "c2": 1.0})
crf_trained = crf.train(
train_sents,
"Models/model.crfCrossValidation1" + str(k) + self.option_tone + self.option_tag + ".tagger",
)
print(str(k) + " fold: crf")
tnt_tagger = tnt.TnT(unk=DefaultTagger("n"), Trained=True, N=100)
tnt_tagger.train(train_sents)
print(str(k) + " fold: tnt")
tag_set = set()
symbols = set()
for i in train_sents:
for j in i:
tag_set.add(j[1])
symbols.add(j[0])
trainer = HiddenMarkovModelTrainer(list(tag_set), list(symbols))
hmm = trainer.train_supervised(train_sents, estimator=lambda fd, bins: LidstoneProbDist(fd, 0.1, bins))
print(str(k) + " fold: hmm")
if last == "U":
lasttagger = UnigramTagger(train_sents, backoff=DefaultTagger("n"))
print(str(k) + " fold: unigram")
if last == "B":
if self.option_tone == "tonal" and self.option_tag == "Affixes":
regex = RegexpTonalSA(DefaultTagger("n"))
if self.option_tone == "tonal" and self.option_tag == "POS":
regex = RegexpTonal(DefaultTagger("n"))
if self.option_tone == "nontonal" and self.option_tag == "Affixes":
regex = RegexpSA(DefaultTagger("n"))
if self.option_tone == "nontonal" and self.option_tag == "POS":
regex = Regexp(DefaultTagger("n"))
dic = dictionary_backoff(self.option_tone, regex)
affix = AffixTagger(train_sents, min_stem_length=0, affix_length=-4, backoff=dic)
lasttagger = BigramTagger(train_sents, backoff=affix)
print(str(k) + " fold: bigram")
to_tag = [untag(i) for i in self.foldlist[self.folds - 1]]
self.crf_tagged += crf.tag_sents(to_tag)
self.tnt_tagged += tnt_tagger.tag_sents(to_tag)
self.hmm_tagged += hmm.tag_sents(to_tag)
self.lasttagger_tagged += lasttagger.tag_sents(to_tag)
self.org_tagged += self.foldlist[self.folds - 1]
self.foldlist = [self.foldlist[self.folds - 1]] + self.foldlist[: (self.folds - 1)]
self.crf = crf
self.tnt = tnt_tagger
self.hmm = hmm
self.lasttagger = lasttagger
org_words = sum(self.org_tagged, [])
self.crf_avg_acc = accuracy(org_words, sum(self.crf_tagged, []))
self.tnt_avg_acc = accuracy(org_words, sum(self.tnt_tagged, []))
self.hmm_avg_acc = accuracy(org_words, sum(self.hmm_tagged, []))
self.lasttagger_avg_acc = accuracy(org_words, sum(self.lasttagger_tagged, []))
print("Accuracy of concatenated crf-tagged sentences: ", self.crf_avg_acc)
print("Accuracy of concatenated tnt-tagged sentences: ", self.tnt_avg_acc)
print("Accuracy of concatenated hmm-tagged sentences: ", self.hmm_avg_acc)
print("Accuracy of concatenated " + last + "-tagged sentences: ", self.lasttagger_avg_acc)
(self.crf_tagprecision, self.crf_tagrecall) = self.tagprecision_recall(crf, self.crf_tagged, self.org_tagged)
(self.tnt_tagprecision, self.tnt_tagrecall) = self.tagprecision_recall(
tnt_tagger, self.tnt_tagged, self.org_tagged
)
(self.hmm_tagprecision, self.hmm_tagrecall) = self.tagprecision_recall(hmm, self.hmm_tagged, self.org_tagged)
(self.lasttagger_tagprecision, self.lasttagger_tagrecall) = self.tagprecision_recall(
lasttagger, self.lasttagger_tagged, self.org_tagged
)
self.org_tagged = []
self.foldlist = []
for i in range(1, self.folds + 1):
self.foldlist.append(self.create_fold(i))