def test(self):
lemmatizer = rulemma.Lemmatizer()
lemmatizer.load()
tokenizer = rutokenizer.Tokenizer()
tokenizer.load()
tagger = rupostagger.RuPosTagger()
tagger.load()
sent = u'Мяукая, голодные кошки ловят жирненьких хрюнделей'
tokens = tokenizer.tokenize(sent)
tags = tagger.tag(tokens)
lemmas = lemmatizer.lemmatize(tags)
for word, tags, lemma, *_ in lemmas:
print(u'{:15}\t{:15}\t{}'.format(word, lemma, tags))
def __init__(self, stopwordsList=None, lang='russian', *args, **kwargs):
nltk.download("stopwords")
#nltk.download("punkt")
self.mystem = Mystem()
self.useLemmas = False
if lang == 'russian':
self.lemmatizer = rulemma.Lemmatizer()
self.lemmatizer.load()
self.tokenizer = rutokenizer.Tokenizer()
self.tokenizer.load()
self.tagger = rupostagger.RuPosTagger()
self.tagger.load()
else:
self.lemmatizer = WordNetLemmatizer()
alphabet = []
self.language = lang
self.tag_dict = {
"J": wordnet.ADJ,
"N": wordnet.NOUN,
"V": wordnet.VERB,
"R": wordnet.ADV
}
if lang == 'russian':
self.stopwords = stopwords.words("russian")
alphabet = "АаБбВвГгДдЕеЁёЖжЗзИиЙйКкЛлМмНнОоПпСсТтУуФфХхЦцЧчШшЩщЪъЫыЬьЭэЮюЯя"
else:
self.stopwords = stopwords.words('english')
alphabet = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
self.stopwords.extend(list(alphabet))
if not stopwordsList is None:
self.stopwords.extend(stopwordsList)
import operator
import rutokenizer
import rupostagger
import rulemma
if __name__ == '__main__':
print('Loading dictionaries and models...')
lemmatizer = rulemma.Lemmatizer()
lemmatizer.load('../tmp/rulemma.dat')
tokenizer = rutokenizer.Tokenizer()
tokenizer.load()
tagger = rupostagger.RuPosTagger()
tagger.load()
print('Loading finished')
sent = u'Мяукая, голодные кошки ловят жирненьких хрюнделей'
tokens = tokenizer.tokenize(sent)
tags = tagger.tag(tokens)
lemmas = lemmatizer.lemmatize(tags)
for word, tags, lemma, *_ in lemmas:
print(u'{:15}\t{:15}\t{}'.format(word, lemma, tags))
tests = [(u'я вижу хрюнделя', u'я видеть хрюндель'),
(u'Мяукая, голодные кошки ловят жирненьких мышек',
u'мяукать , голодный кошка ловить жирненький мышка'),
(u'Мы спрашивали про уроки и оценки',
u'я спрашивать про урок и оценка'),
(u'Куда же улетели облачка?', u'куда же улететь облачко ?')]
def load_samples(data_folder):
logging.info('Loading samples...')
tokenizer = rutokenizer.Tokenizer()
tokenizer.load()
samples = []
emitted = set()
with io.open(os.path.join(data_folder, 'invalid_syntax_dataset.txt'),
'r',
encoding='utf-8') as rdr:
for line in rdr:
if not line.startswith('#'):
words = tokenizer.tokenize(line.strip().lower())
if len(words) > 0:
words = remove_terminator(words)
key = u' '.join(words)
if key not in emitted:
samples.append((words, 0))
emitted.add(key)
# В отдельном файле - валидные (но возможно не всегда разумные) сэмплы
with io.open(os.path.join(data_folder, 'valid_syntax_dataset.txt'),
'r',
encoding='utf-8') as rdr:
for line in rdr:
if not line.startswith('#'):
words = tokenizer.tokenize(line.strip().lower())
if len(words) > 0:
words = remove_terminator(words)
key = u' '.join(words)
if key not in emitted:
samples.append((words, 1))
emitted.add(key)
# Предполагаем, что корпус текстов для N-грамм содержит хорошие образцы
with io.open(os.path.join(data_folder, 'ngrams_corpus.txt'),
'r',
encoding='utf-8') as rdr:
for line in rdr:
if not line.startswith('#'):
words = tokenizer.tokenize(line.strip().lower())
if len(words) > 0:
words = remove_terminator(words)
key = u' '.join(words)
if key not in emitted:
samples.append((words, 0))
emitted.add(key)
for inpath in ['paraphrases.txt', 'pqa_all.dat']:
with io.open(os.path.join(data_folder, inpath), 'r',
encoding='utf-8') as rdr:
for line in rdr:
if not line.startswith('#'):
s = clean_input(line)
words = tokenizer.tokenize(s)
if len(words) > 0:
if u'ты' in words:
words = remove_terminator(words)
key = u' '.join(words)
if key not in emitted:
emitted.add(key)
samples.append((words, 1))
print('sample count={}'.format(len(samples)))
nb0 = sum((label == 0) for (words, label) in samples)
nb1 = sum((label == 1) for (words, label) in samples)
print('nb0={} nb1={}'.format(nb0, nb1))
max_wordseq_len = max(len(words) for (words, label) in samples)
print('max_wordseq_len={}'.format(max_wordseq_len))
return samples
def __init__(self):
import rutokenizer
self.tokenizer = rutokenizer.Tokenizer()
self.tokenizer.load()
