def normalisation(new_data):
m = Mystem()
m.start()
normalize_data = []
data_size = len(new_data)
count = 0
for i in tqdm(new_data, desc="normalisation"):
lemmas = m.lemmatize(i)
normalize_data.append((''.join(lemmas)).replace("\n", ""))
count = count + 1
# print(count*100/data_size)
return normalize_data
import torch
from pymystem3 import Mystem
from itertools import count
import onmt.io
import onmt.translate
import onmt
import onmt.ModelConstructor
import onmt.modules
import sys
from onmt.io.IO import build_dataset_request
m = Mystem()
m.start()
opt = Namespace(
alpha=0.0,
attn_debug=False,
batch_size=1,
beam_size=10,
beta=-0.0,
data_type='text',
dump_beam='',
dynamic_dict=False,
gpu=-1,
max_length=100,
max_sent_length=None,
min_length=0,
model=os.path.dirname(os.path.abspath(__file__)) +
import re, os
import gensim
from gensim import corpora, models
import nltk
from nltk import FreqDist
from nltk.collocations import *
from pymystem3 import Mystem
from stop_words import get_stop_words
ru_stop = get_stop_words('ru')
mystem_object = Mystem()
mystem_object.start()
puncts = "[«–»—!\$%&'()*+,./:;<=>?@^_`{|}~']*-–—...]"
extra_words = [
"понимать", "знать", "хотеть", "глаз", "рука", "голова", "увидеть",
"что-то", "смотреть", "нога", "свой", 'видеть', 'становиться', 'остаться',
'давать', 'стоять', 'оставаться', 'оказываться', 'думать'
]
#Fantasy
def processFileFantasy(file):
doc = []
with open(file, 'r', encoding='utf-8') as f:
#print(file)
text = f.read()
#print(len(text))
words = text.split()
for word in words:
def make_lemmantisation(text):
m = Mystem()
m.start()
lemmas = m.lemmatize(str(text))
return lemmas
parser = argparse.ArgumentParser(description='RE to CONLL')
parser.add_argument('--re', type=str, help='REs to apply')
parser.add_argument('--data_dir', type=str, help='Folder with docs')
parser.add_argument('--file', type=str, help='Source file')
parser.add_argument('--lines', action='store_true', help='Lines as docs')
parser.add_argument('--bioes', action='store_true', help='Output BEOES encoding')
args = parser.parse_args()
def build_re():
patterns = filter(lambda line : line and not line.startswith('#') and not line.isspace(), open(args.re).readlines())
return map(lambda line: re.compile(line.strip().decode('utf8'), flags=re.U+re.M+re.S), patterns)
from pymystem3 import Mystem
mystem = Mystem(grammar_info=False, disambiguation=False)
mystem.start()
def parse_doc(mystem, text):
morph_parse = mystem.analyze(text)
current_pos = 0
offsets = []
lemmas = []
words = []
all_words = []
for word_parse in morph_parse:
word = word_parse['text']
all_words.append(word)
sword = word.strip(' ').replace('\n', u'\u2028')
if re.search("\w", sword, flags=re.U):
words.append(sword)
analysis = word_parse.get('analysis')