def natasha_res(text):
names_extractor = NamesExtractor()
location_extractor = LocationExtractor()
organisation_extractor = OrganisationExtractor()
dates_extractor = DatesExtractor()
money_extractor = MoneyExtractor()
names_mapper = lambda x: text[x.span[0]:x.span[1]]
location_mapper = names_mapper
org_mapper = names_mapper
money_mapper = names_mapper
res = {
'names': set(map(names_mapper, names_extractor(text))),
'locations': set(map(location_mapper, location_extractor(text))),
'organisations': set(map(org_mapper, organisation_extractor(text))),
'dates': set(map(dates_mapper, dates_extractor(text))),
'money': set(map(money_mapper, money_extractor(text))),
}
return res
def natasha_process():
extractors = [NamesExtractor(), PersonExtractor(), OrganisationExtractor()]
result = []
with open(test_data) as file_data:
for i, text in tqdm(enumerate(file_data)):
result.append([extr(text) for extr in extractors])
output = []
for line in result:
output.append({})
for i, extr in enumerate(line):
for match in extr:
output[-1][(match.span[0],
match.span[1])] = 'PERSON' if i != 2 else 'ORG'
with open('res.txt', 'w') as file_res:
with open(test_data, 'r') as file_data:
for line in output:
tmp = []
processed = []
s = file_data.readline()
for key in line:
k1, k2 = key
sub = s[k1:k2]
for el in sub.split(' '):
if k1 in processed:
continue
processed.append(k1)
tmp.append(
str(k1) + " " + str(len(el)) + " " + line[key] +
" ")
#file_res.write(str(k1) + " " + str(len(el)) + " " + line[key] + " ")
k1 += len(el) + 1
tmp.sort()
for l in tmp:
file_res.write(l)
file_res.write('EOL\n')
def __init__(self):
from natasha import (NamesExtractor, SimpleNamesExtractor,
DatesExtractor, MoneyExtractor,
MoneyRateExtractor, MoneyRangeExtractor,
LocationExtractor, AddressExtractor,
OrganisationExtractor, PersonExtractor)
addr_ex = AddressExtractor()
date_ex = DatesExtractor()
loc_ex = LocationExtractor()
money_ex = MoneyExtractor()
money_range_ex = MoneyRangeExtractor()
money_rate_ex = MoneyRateExtractor()
name_ex = SimpleNamesExtractor()
org_ex = OrganisationExtractor()
person_ex = PersonExtractor()
# extractors=[addr_ex, date_ex, loc_ex, money_ex, money_range_ex, money_rate_ex,
# name_ex, org_ex, person_ex]
self.extractors = [
addr_ex, date_ex, loc_ex, money_ex, money_range_ex, money_rate_ex,
org_ex, person_ex
]
def send_db_pool_map(doc):
extractors = [AddressExtractor(), DatesExtractor(), LocationExtractor(), MoneyExtractor(), NamesExtractor(),
OrganisationExtractor(), PersonExtractor()]
pool_local = ThreadPool(10)
ne_full = []
span_ne_full = []
type_ne_full = []
try:
for extr in extractors:
global text_paper
text_paper = doc
matches = extr(text_paper)
ne = pool_local.starmap(get_ne2, zip(matches,[doc for x in range(len(matches))]))
span_ne = pool_local.map(get_span_ne, matches)
type_ne = pool_local.map(get_type_ne, matches)
ne_full.append(ne)
span_ne_full.append(span_ne)
type_ne_full.append(type_ne)
except:
print('Ошибка! Примерный номер =', '?')
pool_local.close()
pool_local.join()
if len(ne_full) != 0:
ne_for_db = reduce(lambda x, y: x + y, ne_full)
span_ne_for_db = reduce(lambda x, y: x + y, span_ne_full)
type_ne_for_db = reduce(lambda x, y: x + y, type_ne_full)
'''if len(ne_for_db) != 0:
cur.execute('UPDATE public.news_rbc '
'SET ne=%s, span_ne=%s, type_ne=%s'
'WHERE id=%s;', (ne_for_db, span_ne_for_db,
type_ne_for_db, num))
con.commit()'''
return [ne_for_db, span_ne_for_db, type_ne_for_db]
else:
return [0, 0, 0]
cur.execute('UPDATE public.news_rbc '
'SET ne=%s, span_ne=%s, type_ne=%s'
'WHERE id=%s;', (ne_for_db, span_ne_for_db,
type_ne_for_db, num))
con.commit()'''
return [ne_for_db, span_ne_for_db, type_ne_for_db]
else:
return [0, 0, 0]
if __name__ == '__main__':
time_begin = time()
# экстракторы
extractors = [AddressExtractor(), DatesExtractor(), LocationExtractor(), MoneyExtractor(), NamesExtractor(),
OrganisationExtractor(), PersonExtractor()]
send_to_db_news_rbc(extractors) # 292.92 секунды
# ошибки: 6911;7168;7561;8246;8539;8691;9211
exit()
'''con = psycopg2.connect(database="texts_politics", user="******", password="******", host="127.0.0.1",
port="5432", )
cur = con.cursor()
pool = ThreadPool(10) # было ошибок 8 - 2459? 2779 = []
for i in tqdm(range(5059,9540,10)): # 296.92 секунды # с3347по3357 не делал
# обработало 5839 строк, из них 120 строк не обработаных
cur.execute("SELECT full_text FROM public.news_rbc ORDER BY id ASC LIMIT 10 OFFSET %s", (i,))
data = cur.fetchall()
docs = [x[0] for x in data]
#new_form = pool.starmap(send_db_pool_map, zip(docs,[i_num for i_num in range(i,i+10)]))
new_form = pool.map(send_db_pool_map,docs) # 281.43 секунды | 293.59
t_for = time()
def extractor():
return OrganisationExtractor()
from ipymarkup import AsciiMarkup, Span, BoxMarkup
import re
import json
from natasha import (NamesExtractor, AddressExtractor, DatesExtractor,
MoneyExtractor, OrganisationExtractor, LocationExtractor)
from natasha.markup import show_markup, show_json
extractors = [
NamesExtractor(),
AddressExtractor(),
DatesExtractor(),
MoneyExtractor(),
OrganisationExtractor(),
LocationExtractor()
]
from flask import Flask
from flask import request
app = Flask(__name__)
@app.route('/getFacts', methods=['POST'])
def getFacts():
print(request.is_json)
content = request.get_json()
text = content['text']
facts = {}
'''
Если ничего не будет работать, это нас спасет
'''
from natasha import NamesExtractor
from natasha import LocationExtractor
from natasha import OrganisationExtractor
from natasha import DatesExtractor
from natasha import AddressExtractor
names_extr = NamesExtractor()
locs_extr = LocationExtractor()
org_extr = OrganisationExtractor()
dates_extr = DatesExtractor()
address_extr = AddressExtractor()
def recognize_names(text):
tmp = text
matches = names_extr(text)
for match in matches:
start, finish = match.span
tmp = tmp.replace(text[start:finish], "[NAME]")
return tmp
def recognize_locs(text):
tmp = text
matches = locs_extr(text)
for match in matches:
start, finish = match.span
def __init__(self):
self.address = AddressExtractor()
self.org = OrganisationExtractor()
self.dates = DatesExtractor()
from natasha import NamesExtractor, OrganisationExtractor
import re
docs = []
with open('dataset_40163_1.txt', 'r') as reader:
for line in reader:
docs.append(line)
names_extractor = NamesExtractor()
orgs_extractor = OrganisationExtractor()
with open('result.txt', 'w+') as writer:
for doc in docs:
for match in names_extractor(doc):
ner_len = 0
ner_start = match.span[0]
for idx in range(*match.span):
if re.match(r'\w', doc[idx]):
if ner_len == 0:
ner_start = idx
ner_len += 1
elif ner_len > 0:
writer.write(f"{ner_start} {ner_len} PERSON ")
ner_len = 0
if ner_len > 0:
writer.write(f"{ner_start} {ner_len} PERSON ")
ner_len = 0
for match in orgs_extractor(doc):
