def ap(train_path, test_path):
modelref = 'ap-' + md5(('ap///' + train_path).encode()).hexdigest() + '.pickle'
test_sentences = list(gen_corpus(test_path))
if not isfile(modelref):
start = perf_counter()
training_sentences = list(gen_corpus(train_path))
ap_model = PerceptronTagger(load=False)
ap_model.train(list(convert_sents_to_zipped(training_sentences)), save_loc=modelref)
end = perf_counter()
print('Training took {} ms.'.format(int((end - start) * 1000)))
else:
ap_model = PerceptronTagger(load=False)
ap_model.load(modelref)
print('Model loaded from file.')
# Evaluation
start = perf_counter()
y_pred, y_true = [], []
for words, tags in test_sentences:
y_pred.extend(y for x, y in ap_model.tag(words))
y_true.extend(tags)
end = perf_counter()
print('Testing took {} ms.'.format(int((end - start) * 1000)))
for l in classification_report(y_true, y_pred).split('\n'):
print(l)
class Tagger(AbstractTagger):
def __init__(self):
self._tagger = PerceptronTagger(load=False)
self._name = 'nltkperceptron'
self._model_name = "nltkperceptron"
self._result = None
super().__init__()
def _save_model(self, fpath):
with open(fpath, 'wb') as f:
dill.dump(self._tagger, f)
def load(self, path=''):
if path == '':
self._load_model(path)
else:
mpath = os.path.join(path, self.model_name)
self._load_model(mpath)
def _load_model(self, fpath):
if fpath == '':
self._tagger = PerceptronTagger(load=True)
else:
with open(fpath, 'rb') as f:
self._tagger = dill.load(f)
def tag(self, data):
res = self._tagger.tag(data)
return [x[1] for x in res]
def train(self, data):
# Reset tagger.
self._tagger = PerceptronTagger(load=False)
self._tagger.train(data)
@property
def produces_temp_data(self):
return False
@property
def requires_additional_params(self):
return False
def set_additional_params(self, options):
pass
def add_temp_dir(self, options):
pass
@property
def model_name(self):
return self._model_name
@property
def name(self):
return self._name
def nltk_perceptron_pos_tagger(input_dict):
name= 'PerceptronPosTagger'
if not input_dict['training_corpus']:
perceptron_tagger = PerceptronTagger()
name += '-pretrained'
else:
perceptron_tagger = PerceptronTagger(load=False)
chunk = input_dict['training_corpus']['chunk']
corpus = input_dict['training_corpus']['corpus']
training_corpus=corpus_reader(corpus, chunk)
perceptron_tagger.train(list(training_corpus))
return {'pos_tagger': {
'function':'tag_sents',
'object': perceptron_tagger,
'name': name
}
}
def _get_pretrain_model():
pos_tagger = PerceptronTagger(load=False)
pos_tagger.train(sentences=train_sents, save_loc=PICKLE)
print('Accuracy : ', pos_tagger.evaluate(test_sents))
def get_tagger(dfs_corpus, word_key="word", pos_key="universal_dependency"):
'''
This function instantiates a tagger trained with some corpus annotations (out of a DataFrame)
Args:
dfs_corpus: one (or a list of) Pandas DataFrame(s) with annotated corpus data
word_key: (default 'word') column name for wordforms in dfs_corpus
pos_key: (default 'universal_dependency') column name for parts-of-speech in dfs_corpus
Returns:
a PerceptronTagger instance
>>> # get a tagger, trained with df_corpus: a Pandas DataFrame with lots of corpus data
>>> tagger = get_tagger(df_corpus)
>>> # tag a sentence now
>>> sentence = 'Here is some beautiful sentence'
>>> tagged_sentence = tagger.tag( sentence.split() )
>>> print(tagged_sentence)
'''
sentences = []
# The algorithm expects a list of DataFrames by default, so make sure we have just that
if isinstance(dfs_corpus, pd.DataFrame):
dfs_corpus = [dfs_corpus]
for df_corpus in dfs_corpus:
# The corpus DataFrame consists of a number of sentences (rows) with a fixed number of tokens.
# Each token has a fixed number of layers holding info like: lemma, wordform or part-of-speech.
# As a result, the number of columns of each row = [number of tokens] x [number of layers]
# To be able to feed the tagger correctly, we need to compute the number of layers,
# so we can infer the number of tokens the sentences hold. This is because
# the tagger expects us to feed it with arrays with length = [number of tokens], as elements of
# one single array holding all sentences arrays (see below).
# So, determine how many layers (lemma, pos, wordform) we have
column_names = list(df_corpus.columns.values)
for n, val in enumerate(column_names):
# remove the numbers at the end of the layers names (lemma 1, lemma 2, ..., pos 1, pos 2, ...)
# so we end up with clean layers name only
column_names[n] = val.split(' ')[0]
number_of_layers = len(set(column_names))
# Now we can determine the standard length of our corpus sentences: that can be computed
# by dividing the number of columns of the corpus DataFrame by the number of layers
# we just computed.
nr_of_words_per_sentence = int( df_corpus.shape[1] / number_of_layers )
# Build training data for the tagger in the right format
# The input must be like: [ [('today','NN'),('is','VBZ'),('good','JJ'),('day','NN')], [...] ]
for index, row in df_corpus.iterrows():
one_sentence = []
wrong = False
for i in range(0, nr_of_words_per_sentence, 1):
word_idx = word_key+' '+str(i)
pos_idx = pos_key+' '+str(i)
try:
tuple = ( row[word_idx], _cut_off_features(row[pos_idx]) )
one_sentence.append( tuple )
if (row[word_idx] is None or row[pos_idx] is None):
wrong = True
except:
raise ValueError("function get_tagger() expects corpus data with columns '%s' and '%s', but those columns could not be found. Please call the function with these extra paramters to declare which column your corpus data has instead: get_tagger(word_key='...', pos_key='...')." % (word_key, pos_key))
if wrong is False:
sentences.append(one_sentence)
# Instantiate and train the tagger now
tagger = PerceptronTagger(load=False)
tagger.train(sentences)
return tagger
https://github.com/evanmiltenburg/Dutch-tagger
We got a POS tagger and a CHUNK tagger. In combination they can be used to apply NER...
"""
import os
import nltk
from nltk.tag.perceptron import PerceptronTagger
from nltk.corpus import alpino as alp # Trained on ALP data.
tagger = PerceptronTagger(load=False)
os.chdir(r'D:\nlp_lib')
tagger.load('model.perc.dutch_tagger_small.pickle'
) # I don't know the source of training data
# Tag a sentence.
tagger.tag('Alle vogels zijn nesten begonnen , behalve ik en jij .'.split())
training_corpus = alp.tagged_sents()
unitagger = nltk.tag.UnigramTagger(training_corpus)
bitagger = nltk.tag.BigramTagger(training_corpus, backoff=unitagger)
perctagger = PerceptronTagger(load=True) # What does load=True mean??
perctagger.train(training_corpus)
sent = 'NLTK is een goeda taal voor NLP'.split()
bitagger.tag(sent)
unitagger.tag(sent)
perctagger.tag(sent)
aḍris = []
#Construction de la chaine d'apprentissage à partir du corpus kabyle
for izirig in open("c:/tal/corpus-kab.txt", encoding='utf-8'):
if (amenzu != 0):
#ligne=ligne.lower()
amenzu = 1
izirig = izirig.replace("\n", "")
a = izirig.split(" ")
for i in a:
b = i.split("/")
try:
if (b[1] != 'NMP'):
b[0] = b[0].lower()
else:
b[0] = b[0].lower()
except:
print(b, 'erreur')
exit()
kab_tags_words1.append((b[0], b[1]))
aḍris.append(kab_tags_words1)
else:
amenzu = 1
#entrainer l'algorithme sur la base du corpus et enregistrer le modèle
trained_model = "c:/tal/trained_model15.pickle"
tagger.train(aḍris, save_loc=trained_model, nr_iter=20)
comb_results = np.zeros((5, 4))
ind_results = np.zeros((5, 4))
for ki in range(data_batch):
from nltk import TnT
from nltk.tag import hmm
from nltk.tag.perceptron import PerceptronTagger
from nltk.tag import CRFTagger
perc_tagger = PerceptronTagger(load=False)
tnt_tagger = TnT()
crf_tagger = CRFTagger()
tnt_tagger.train(training_data[ki])
hmm_tagger = nltk.HiddenMarkovModelTagger.train(training_data[ki])
perc_tagger.train(training_data[ki])
crf_tagger.train(training_data[ki], 'model.crf.tagger')
# t.tagdata(test_data[800:])
perc_pred = []
hmm_pred = []
for i in testing_data[ki]:
perc_pred.append(perc_tagger.tag(i))
hmm_pred.append(hmm_tagger.tag(i))
crf_pred = crf_tagger.tag_sents(testing_data[ki])
tnt_pred = tnt_tagger.tagdata(testing_data[ki])
pred = {'p': perc_pred, 'h': hmm_pred, 'c': crf_pred, 't': tnt_pred}
def most_frequent(List):
import cowparser as cp
train_sents = []
test_sents = []
gen = cp.sentences_for_dir(separate=False)
for i, (metadata, data) in enumerate(gen):
train_sents.append([(a,b) for a,b,c in data])
if i == 2000000:
break
for i, (metadata, data) in enumerate(gen):
test_sents.append([(a,b) for a,b,c in data])
if i == 5000:
break
from nltk.tag.perceptron import PerceptronTagger
pt = PerceptronTagger(load=False)
pt.train(train_sents,'model2.perc.dutch_tagger')
print(pt.evaluate(test_sents))