def extract_names(text):
"""Извлекает имена из текста"""
doc = Doc(text)
doc.segment(segmenter)
doc.tag_morph(morph_tagger)
doc.parse_syntax(syntax_parser)
doc.tag_ner(ner_tagger)
for span in doc.spans:
if span.type == PER:
span.normalize(morph_vocab)
span.extract_fact(names_extractor)
names = [{
'normal': _.normal,
'fio': _.fact.as_dict,
'start': _.start,
'end': _.stop
} for _ in doc.spans if _.fact]
return names
def extract_entities(text: str) -> List[NamedEntity]:
"""Распознавание именованных сущностей"""
doc = Doc(text)
doc.segment(segmenter)
doc.tag_ner(ner_tagger)
doc.tag_morph(morph_tagger)
for token in doc.tokens:
token.lemmatize(morph_vocab)
for span in doc.spans:
span.normalize(morph_vocab)
entities = []
for sentence in doc.sents:
for span in sentence.spans:
ent = NamedEntity(text=span.text,
type=span.type,
norm=span.normal,
sentence=sentence.text)
entities.append(ent)
return entities
def ner(text: str) -> set:
"""Распознавание именованных сущностей
"""
warnings.warn('К удалению. Заменится на `extract_entities`',
DeprecationWarning)
doc = Doc(text)
doc.segment(segmenter)
doc.tag_ner(ner_tagger)
doc.tag_morph(morph_tagger)
for token in doc.tokens:
token.lemmatize(morph_vocab)
for span in doc.spans:
span.normalize(morph_vocab)
ner_tokens = []
for span in doc.spans:
if len(span.normal) > 0:
ner_tokens.append((span.normal, span.type))
else:
ner_tokens.append((span.text, span.type))
return set(ner_tokens)
def ne_loc_extraction(text, del_names=False, del_addr=False):
#Extract, normalize and delete (optional) named entities and locations
doc = Doc(text)
doc.segment(segmenter)
doc.tag_ner(ner_tagger)
doc.tag_morph(morph_tagger)
for span in doc.spans:
span.normalize(morph_vocab)
for span in doc.spans:
if span.type == 'PER':
span.extract_fact(names_extractor)
if span.type == 'LOC':
span.extract_fact(addr_extractor)
if del_names:
for span in doc.spans:
if span.type == 'PER':
text = text.replace(span.text, '')
if del_addr:
for span in doc.spans:
if span.type == 'LOC':
text = text.replace(span.text, '')
normal_ne_loc = {}
normal_ne_loc['NAMES'] = [
span.normal for span in doc.spans if span.type == 'PER'
]
normal_ne_loc['LOCATIONS'] = [
span.normal for span in doc.spans if span.type == 'LOC'
]
return text, normal_ne_loc
class NatashaExtractor:
def __init__(self, text: str):
self.doc = Doc(text)
self.doc.segment(segmenter)
self.doc.tag_morph(morph_tagger)
self.doc.parse_syntax(syntax_parser)
self.doc.tag_ner(ner_tagger)
for span in self.doc.spans:
span.normalize(morph_vocab)
def find_locations(self) -> List[str]:
locations = list(
filter(lambda span: span.type == 'LOC', self.doc.spans))
return list(map(lambda span: span.normal, locations))
def find_date(self) -> List[date]:
matched_obj: List[Match] = list(dates_extractor(self.doc.text))
natasha_found_dates = list(
map(lambda x: parse_natasha_date_to_datetime(x.fact), matched_obj))
return find_dates_as_word(self.doc.text) + natasha_found_dates
def extract_entities(text: str):
"""Returns dictionry with all recognized entities in format
{
locations: [],
peaple: [],
orginizations: [],
money: []
}
"""
doc = Doc(text)
doc.segment(segmenter)
doc.tag_morph(morph_tagger)
doc.parse_syntax(syntax_parser)
doc.tag_ner(ner_tagger)
for span in doc.spans:
span.normalize(morph_vocab)
locations = list(filter(lambda span: span.type == 'LOC', doc.spans))
locations = list(set(location.normal for location in locations))
orginizations = list(filter(lambda span: span.type == 'ORG', doc.spans))
orginizations = list(set(org.normal for org in orginizations))
people = list(filter(lambda span: span.type == 'PER', doc.spans))
people = list(set(person.normal for person in people))
money = list(match.fact for match in money_extractor(text))
money = list(set(f'{m.amount} {m.currency}' for m in money))
return {
'locations': locations,
'people': people,
'orginizations': orginizations,
'money': money
}
# text = 'Минздрав Украины проверит медицинские учреждения Харьковской, Одесской и Запорожской областей из-за того, что они не до конца использовали индийскую вакцину от коронавируса Covishield компании AstraZeneca из первой партии. Об этом сегодня, 23 апреля, во время брифинга сказал главный государственный санитарный врач Виктор Ляшко. По его словам, только в трех областях до сих пор не использовали полностью вакцину Covishield из первой партии, нарушив тем самым указания Минздрава. Ляшко сообщил, что с 26 апреля в Харьковскую, Одесскую и Запорожскую области направятся представители Минздрава, чтобы выяснить, почему сложилась такая ситуация. Напомним, что в Украине вакцинация от коронавируса началась 24 февраля 2021 года. По состоянию на утро 23 апреля прививки получили 508 046 человек. Из них пять человек получили две дозы вакцины. Ранее сообщалось, что с начала пандемии в Украине по состоянию на утро 23 апреля 2021 года было подтверждено 2 004 630 случаев СOVID-19. Выздоровели 1 552 267 человек, а 41 700 – умерли.'
# print(extract_entities(text))
def identify_gender(doc, name=None):
name_gender = None
if name is not None:
namedoc = Doc(name)
namedoc.segment(segmenter)
namedoc.tag_morph(morph_tagger)
namedoc.tag_ner(ner_tagger)
if len(namedoc.spans) > 0 and namedoc.spans[0].type == "PER":
name_gender = mode([
token.feats.get("Gender") for token in namedoc.spans[0].tokens
if token.feats.get("Gender") is not None
])
if type(doc) == str:
doc = Doc(doc)
doc.segment(segmenter)
doc.tag_morph(morph_tagger)
doc.parse_syntax(syntax_parser)
doc.tag_ner(ner_tagger)
genders = {"Fem": 0, "Masc": 0, None: 0}
for token in doc.tokens:
if token.pos in ["PRON"]: #["VERB", "AUX", "ADJ"]:
sent, num = map(lambda x: int(x) - 1, token.head_id.split("_"))
head = doc.sents[sent].tokens[num]
if token.rel in ["nsubj"] and token.feats.get(
"Person") == '1' and head.pos in ["VERB", "AUX", "ADJ"]:
genders[head.feats.get("Gender")] += 1
# if token.feats.get("Person") == '1' or head.pos == "PRON" and head.feats.get("Person") == '1':
# genders[token.feats.get("Gender")] += 1
genders[name_gender] += 0.25 * (genders["Masc"] + genders["Fem"] + 1
) # some threshold
del genders[None]
return max(genders, key=genders.get)
syntax_parser = NewsSyntaxParser(emb)
ner_tagger = NewsNERTagger(emb)
morph_vocab = MorphVocab()
names_extractor = NamesExtractor(morph_vocab)
money_extractor = MoneyExtractor(morph_vocab)
text = 'Посол Израиля на Украине Йоэль Лион признался, что пришел в шок, узнав о решении властей Львовской области объявить 2019 год годом лидера запрещенной в России Организации украинских националистов (ОУН) Степана Бандеры...'
docType = 'coast'
doc = Doc(text)
doc.segment(segmenter)
doc.tag_morph(morph_tagger)
doc.parse_syntax(syntax_parser)
doc.tag_ner(ner_tagger)
for span in doc.spans:
span.normalize(morph_vocab)
#для судебного приказа
if docType == 'coast':
#фио
for span in doc.spans:
if span.type == PER:
span.extract_fact(names_extractor)
x = [_.fact.as_dict for _ in doc.spans if _.type == PER]
if x:
res['ФИО'] = x
else:
status = 0
display(doc.sents[:5])
# In[29]:
new_sent = doc.sents
new_sent_list = [_.text for _ in new_sent]
# In[69]:
#create new list of NER in each sentence
list_of_ner = []
for s in new_sent_list:
list = []
doc1 = Doc(s)
doc1.segment(segmenter)
doc1.tag_ner(ner_tagger)
doc1.tag_morph(morph_tagger)
for span in doc1.spans:
span.normalize(morph_vocab)
if (span.type == 'PER'):
list.append(span.normal)
list_of_ner.append(list)
# In[135]:
important_per = {
'Путин': 'Владимир Путин',
'Байден': 'Джо Байден',
'Трамп': 'Дональд Трамп',
'Навальный': 'Алексей Навальный',
'Маск': 'Илон Маск',
def transform_text(text, gender, narrator):
transformed = copy(text)
doc = Doc(text)
doc.segment(segmenter)
doc.tag_morph(morph_tagger)
doc.parse_syntax(syntax_parser)
doc.tag_ner(ner_tagger)
ambiguous = find_ambiguous_pronouns(gender, doc, narrator)
print(ambiguous)
global_offset = 0
for token in doc.tokens:
new_word = None
if token.pos == "VERB" or token.pos == "PRON" and token.feats.get("Person") == '1':
new_word = make_replacement(token.text, gender,
token.feats.get("Number", None),
token.feats.get("Case", None))
elif token.pos == "DET":
if token.text in SELFDETERMINERS:
continue
elif token.text.lower() in MYDETERMINERS:
sentence = int(token.id.split("_")[0]) - 1
head = int(token.head_id.split("_")[1]) - 1
curid = int(token.id.split("_")[1]) - 1
if curid - head > 3: # some threshold
word2insert = doc.sents[sentence - 1].tokens[head]
word2insert = change_case(word2insert.text, token.feats.get("Case", None))
print(f"you need to insert {word2insert} after {token.text}")
new_determiner = MYDETERMINERS[token.text.lower()]
transformed = new_determiner.join(
[transformed[:token.start + global_offset], transformed[token.stop + global_offset:]])
global_offset += len(new_determiner) - len(token.text)
transformed = word2insert.join([transformed[:token.stop + 1 + global_offset] + " ",
transformed[token.stop + 1 + global_offset:]])
global_offset += len(word2insert) + 1
elif curid - head < 0:
new_determiner = make_replacement(token.text, gender, token.feats.get("Number", None), token.feats.get("Case", None))
transformed = new_determiner.join(
[transformed[:token.start + global_offset], transformed[token.stop + global_offset:]])
global_offset += len(new_determiner) - len(token.text)
continue
if token.id in ambiguous:
ambiguous_replace = ambiguous[token.id]
if ambiguous_replace[0] == ambiguous_replace[1]:
continue
# transformed = "".join([transformed[token.start + global_offset:], transformed[:token.stop + global_offset]])
# global_offset -= len(token.text)
transformed = ambiguous_replace[2].join(
[transformed[:ambiguous_replace[0] + global_offset],
transformed[ambiguous_replace[1] + global_offset:]])
global_offset += len(ambiguous_replace[2]) - len(token.text)
continue
if new_word is not None:
transformed = new_word.join([transformed[:token.start + global_offset],
transformed[token.stop + global_offset:]])
global_offset += len(new_word) - len(token.text)
doc.syntax.print()
print()
print(*doc.tokens, sep="\n")
return transformed
def transform_text(text, narrator):
"""
Transform the given text from the first person to third
:param text: the text itself
:param narrator: the name of the narrator
:return:
"""
narrator = narrator.strip()
if narrator == "":
narrator = "Рассказчик"
transformed = copy(text)
doc = Doc(text)
doc.segment(segmenter)
doc.tag_morph(morph_tagger)
doc.parse_syntax(syntax_parser)
doc.tag_ner(ner_tagger)
gender = identify_gender(doc)
quotes1 = analyze_quotes(doc)
ambiguous = find_ambiguous_pronouns(gender, doc, narrator, quotes1)
print(ambiguous)
offsets = [0] * len(doc.tokens)
quotes = {}
changes = []
print(quotes1)
for i, token in enumerate(doc.tokens):
new_word = None
if quotes1[i]:
continue
if token.pos == "VERB" or token.pos == "PRON" and token.feats.get("Person") == '1':
# simple case with verb or pronoun of the first person
if token.text.lower() in verb_mapping:
new_word = verb_mapping[token.text.lower()]
else:
new_word = make_replacement(token.text, gender,
token.feats.get("Number", None),
token.feats.get("Case", None))
if new_word.lower().strip() == token.text.lower().strip():
new_word = None
elif token.pos == "DET":
if token.text in SELFDETERMINERS:
continue
elif token.text.lower() in MYDETERMINERS:
sentence = int(token.id.split("_")[0]) - 1
head = int(token.head_id.split("_")[1]) - 1
curid = int(token.id.split("_")[1]) - 1
if curid - head > 3: # some threshold; if there is a determiner after the object
word2insert = doc.sents[sentence - 1].tokens[head]
word2insert = change_case(word2insert.text, token.feats.get("Case", None))
# print(f"you need to insert {word2insert} after {token.text}")
new_determiner = MYDETERMINERS[token.text.lower()]
transformed = new_determiner.join(
[transformed[:token.start + sum(offsets[:i])], transformed[token.stop + sum(offsets[:i]):]])
make_change(changes, token.start, token.start + len(new_determiner), i)
offsets[i] += len(new_determiner) - len(token.text)
transformed = word2insert.join([transformed[:token.stop + 1 + sum(offsets[:i + 1])] + " ",
transformed[token.stop + 1 + sum(offsets[:i + 1]):]])
make_change(changes, token.stop + 1, token.stop + len(word2insert) + 1 + 1, i)
offsets[i] += len(word2insert) + 1
elif curid - head < 0:
new_determiner = make_replacement(token.text, gender, token.feats.get("Number", None),
token.feats.get("Case", None))
transformed = new_determiner.join(
[transformed[:token.start + sum(offsets[:i])], transformed[token.stop + sum(offsets[:i]):]])
make_change(changes, token.start, token.start + len(new_determiner), i)
offsets[i] += len(new_determiner) - len(token.text)
else:
new_determiner = MYDETERMINERS[token.text.lower()]
transformed = new_determiner.join(
[transformed[:token.start + sum(offsets[:i])], transformed[token.stop + sum(offsets[:i]):]])
make_change(changes, token.start, token.start + len(new_determiner), i)
offsets[i] += len(new_determiner) - len(token.text)
continue
if token.id in ambiguous:
ambiguous_replace = ambiguous[token.id]
if ambiguous_replace[0] == ambiguous_replace[1]:
transformed = "".join(
[transformed[:token.start + sum(offsets[:i])], transformed[token.stop + sum(offsets[:i]):]])
# make_change(changes, token.start - 1, token.stop, i)
offsets[doc.tokens.index(token)] -= len(token.text)
sent, num = map(lambda x: int(x) - 1, ambiguous_replace[3].split('_'))
another_token = doc.sents[sent].tokens[num]
ind = doc.tokens.index(another_token)
transformed = (" " + ambiguous_replace[2]).join([transformed[:another_token.stop + sum(offsets[:ind])],
transformed[
another_token.stop + sum(offsets[:ind]):]])
make_change(changes, token.stop, token.stop + len(ambiguous_replace[2]) + 1, i)
offsets[ind] += len(ambiguous_replace[2]) + 1
continue
transformed = ambiguous_replace[2].join(
[transformed[:ambiguous_replace[0] + sum(offsets[:i])],
transformed[ambiguous_replace[1] + sum(offsets[:i]):]])
make_change(changes, ambiguous_replace[0], ambiguous_replace[0] + len(ambiguous_replace[2]), i)
offsets[i] += len(ambiguous_replace[2]) - len(token.text)
continue
if new_word is not None:
transformed = new_word.join([transformed[:token.start + sum(offsets[:i])],
transformed[token.stop + sum(offsets[:i]):]])
make_change(changes, token.start, token.start + len(new_word), i)
offsets[i] += len(new_word) - len(token.text)
# doc.syntax.print()
# capitalize all the first letters in each sentence
for sent in doc.sents:
offset = sum(offsets[:doc.tokens.index(sent.tokens[0])])
transformed = transformed[sent.start + offset].upper().join(
[transformed[:sent.start + offset], transformed[sent.start + offset + 1:]])
cum_offsets = [0] + list(accumulate(offsets))
for i, change in enumerate(changes):
token_i = change[2]
changes[i] = (change[0] + cum_offsets[token_i], change[1] + cum_offsets[token_i])
# print(*doc.tokens, sep="\n")
# for change in changes:
# print(change, transformed[change[0]:change[1]])
return transformed, changes
class TextProcessing:
def __init__(self, text):
self.doc = Doc(text)
self.doc.segment(Segmenter())
self.doc.tag_morph(NewsMorphTagger(NewsEmbedding()))
morph_vocab = MorphVocab()
for token in self.doc.tokens:
token.lemmatize(morph_vocab)
self.doc.parse_syntax(NewsSyntaxParser(NewsEmbedding()))
self.doc.tag_ner(NewsNERTagger(NewsEmbedding()))
for span in self.doc.spans:
span.normalize(morph_vocab)
self.words = tuple(filter(lambda x: x.pos not in ('X', 'PUNCT'), self.doc.tokens))
self.tokens_nouns = tuple(filter(lambda t: t.pos in ['NOUN', 'PROPN'], self.doc.tokens))
self.tokens_adjs = tuple(filter(lambda t: t.pos == 'ADJ', self.doc.tokens))
self.tokens_verbs = tuple(filter(lambda t: t.pos == 'VERB', self.doc.tokens))
def unique_lemmas(self, pos=None):
if pos is None:
return tuple(set(dt.lemma for dt in self.doc.tokens))
else:
return tuple(set(dt.lemma for dt in filter(lambda dt: dt.pos == pos, self.doc.tokens)))
def unique_words(self, pos=None):
if pos is None:
return tuple(set(dt.lemma for dt in self.words))
else:
return tuple(set(dt.lemma for dt in filter(lambda dt: dt.pos == pos, self.words)))
def word_usages(self):
return tuple(dt.text for dt in self.words)
def token_usages(self):
return tuple(dt.text for dt in self.doc.tokens)
def unique_word_usages(self):
return tuple(set(self.word_usages()))
def unique_token_usages(self):
return tuple(set(self.token_usages()))
def omonyms_freq_compute(self, include_stopwords=True):
wu = self.word_usages() if include_stopwords else sw_filter(self.word_usages())
wu_repeats = {i: wu.count(i) for i in wu}
res = []
for case in wu_repeats.items():
absolute = case[1]
if absolute > 1:
relative = round((absolute / len(self.words if include_stopwords else sw_filter(self.words))) * 100)
text = case[0]
res.append((text, absolute, relative))
return tuple(res)
def avg_sent_len(self):
return round(sum(map(lambda s: len(s.tokens), self.doc.sents)) / len(self.doc.sents))
def total_word_usages(self):
return len(self.words)
def total_lemma_usages(self):
return len(self.doc.tokens)
def pos_freq_compute(self):
tokens_by_poses = []
for pos in chain(*parts_of_speech.keys()):
words_of_pos = tuple(filter(lambda dt: dt.pos == pos, self.words))
absolute_words_usages = len(words_of_pos)
if absolute_words_usages != 0:
relative_word_usages = round((absolute_words_usages / len(self.words)) * 100)
pos_translated = pos_name_to_rus(pos, True)
absolute_unique_words = len(self.unique_words(pos))
relative_unique_words = round((absolute_unique_words / len(self.unique_words())) * 100)
tokens_by_poses.append((absolute_words_usages, relative_word_usages,
pos, pos_translated,
absolute_unique_words, relative_unique_words))
return tuple(tokens_by_poses)
def nouns_adj_by_cases(self):
nouns = tuple(filter(lambda t: 'Case' in dict(t.feats).keys(), self.tokens_nouns))
adjs = tuple(filter(lambda t: 'Case' in dict(t.feats).keys(), self.tokens_adjs))
return tuple((case,
tuple(filter(lambda t: t.feats['Case'] == case, nouns)),
tuple(filter(lambda t: t.feats['Case'] == case, adjs))) for case in cases)
def case_analysis(self):
nabc = self.nouns_adj_by_cases()
result = []
for i, case in enumerate(cases):
abs_nouns = len(nabc[i][1])
abs_adj = len(nabc[i][2])
rel_nouns = abs_nouns / len(self.tokens_nouns)
rel_nouns = round(rel_nouns * 100)
rel_adj = abs_adj / len(self.tokens_adjs)
rel_adj = round(100 * rel_adj)
abs_sum = abs_nouns + abs_adj
rel_sum = round((abs_sum / (len(self.tokens_adjs) + len(self.tokens_nouns))) * 100)
result.append((case,
abs_nouns, rel_nouns,
abs_adj, rel_adj,
abs_sum, rel_sum))
return tuple(result)
def verb_form_analysis_tense(self):
verbs = tuple(filter(lambda t: 'Tense' in dict(t.feats).keys(), self.tokens_verbs))
return tuple((tense, len(tuple(filter(lambda t: t.feats['Tense'] == tense, verbs))))
for tense in ('Past', 'Pres', 'Fut'))
def verb_form_analysis_person(self):
verbs = tuple(filter(lambda t: 'Person' in dict(t.feats).keys(), self.tokens_verbs))
return tuple((p, len(tuple(filter(lambda t: t.feats['Person'] == p, verbs))))
for p in ('1', '2', '3'))
def verb_form_analysis_number(self):
verbs = tuple(filter(lambda t: 'Number' in dict(t.feats).keys(), self.tokens_verbs))
return tuple((p, len(tuple(filter(lambda t: t.feats['Number'] == p, verbs))))
for p in ('Sing', 'Plur'))
def simple_summarization(self, top=None):
if top is None:
top = len(self.doc.sents) * 0.20
if top < 1:
top = 1
else:
top = round(top)
words = sw_filter(self.words)
lemma_frequencies = {}
for word in words:
if word.lemma not in lemma_frequencies.keys():
lemma_frequencies[word.lemma] = 1
else:
lemma_frequencies[word.lemma] += 1
max_frequency = max(lemma_frequencies.values())
sent_scores = {}
for sentence in self.doc.sents:
# Cумма относительных частот словоупотреблений для каждого предложения текста
sent_scores[sentence.text] = sum(tuple(lemma_frequencies[word.lemma] / max_frequency
for word in filter(lambda w: w.lemma in lemma_frequencies.keys(),
sentence.tokens)))
summary_sentences = nlargest(top, sent_scores.items(), key=lambda item: item[1])
return tuple(t for t, _ in summary_sentences)
def ner_stats(self):
return (len(tuple(filter(lambda s: s.type == 'PER', self.doc.spans))),
len(tuple(filter(lambda s: s.type == 'LOC', self.doc.spans))),
len(tuple(filter(lambda s: s.type == 'ORG', self.doc.spans))))
def top_ners(self):
pers = dict(Counter(tuple(map(lambda s: s.normal, filter(lambda s: s.type == 'PER', self.doc.spans)))))
locs = dict(Counter(tuple(map(lambda s: s.normal, filter(lambda s: s.type == 'LOC', self.doc.spans)))))
orgs = dict(Counter(tuple(map(lambda s: s.normal, filter(lambda s: s.type == 'ORG', self.doc.spans)))))
return sorted(pers.items(), key=lambda x: x[1], reverse=True), sorted(locs.items(), key=lambda x: x[1], reverse=True), sorted(orgs.items(),key=lambda x: x[1], reverse=True)
def Main(docType, text):
status = 1
res = {}
segmenter = Segmenter()
emb = NewsEmbedding()
morph_tagger = NewsMorphTagger(emb)
syntax_parser = NewsSyntaxParser(emb)
ner_tagger = NewsNERTagger(emb)
morph_vocab = MorphVocab()
names_extractor = NamesExtractor(morph_vocab)
money_extractor = MoneyExtractor(morph_vocab)
doc = Doc(text)
doc.segment(segmenter)
doc.tag_morph(morph_tagger)
doc.parse_syntax(syntax_parser)
doc.tag_ner(ner_tagger)
for span in doc.spans:
span.normalize(morph_vocab)
#для судебного приказа
if docType == 'coast':
#фио
for span in doc.spans:
if span.type == PER:
span.extract_fact(names_extractor)
x = [_.fact.as_dict for _ in doc.spans if _.type == PER]
if x:
res['ФИО'] = x
else:
status = 0
#инн
y = myextractors.findINN(text)
if y:
res['ИНН'] = y
else:
status = 0
#номер судебного приказа
y = myextractors.findNCOASTCASE(text)
if y:
res['номер судебного приказа'] = y
else:
status = 0
#дата с п
y = myextractors.findDATECOAST(text)
if y:
res['дата судебного приказа'] = y
else:
status = 0
#организации
y = []
for span in doc.spans:
if span.type == ORG:
d = {}
d['name'] = span.text
y = y + [d]
if y:
res['организации'] = y
else:
status = 0
#для письма
if docType == 'mail':
#фио
for span in doc.spans:
if span.type == PER:
span.extract_fact(names_extractor)
x = [_.fact.as_dict for _ in doc.spans if _.type == PER]
if x:
res['ФИО'] = x
else:
status = 0
#инн
y = myextractors.findINN(text)
if y:
res['ИНН'] = y
else:
status = 0
#номер дог
y = myextractors.findNCONTRACT(text)
if y:
res['номер договора'] = y
else:
status = 0
#дата дог
y = myextractors.findDATECONT(text)
if y:
res['дата договора'] = y
else:
status = 0
#для платежного поручения
if docType == 'order':
#фио
for span in doc.spans:
if span.type == PER:
span.extract_fact(names_extractor)
x = [_.fact.as_dict for _ in doc.spans if _.type == PER]
if x:
res['ФИО'] = x
else:
status = 0
#инн
y = myextractors.findINN(text)
if y:
res['ИНН'] = y
else:
status = 0
#организации
y = []
for span in doc.spans:
if span.type == ORG:
d = {}
d['name'] = span.text
y = y + [d]
if y:
res['организации'] = y
else:
status = 0
#номер дог
y = myextractors.findNCONTRACT(text)
if y:
res['номер договора'] = y
else:
status = 0
#дата дог
y = myextractors.findNCONTRACT(text)
if y:
res['номер договора'] = y
else:
status = 0
#сумма
matches = list(money_extractor(text))
y = [_.fact for _ in matches]
ret = []
for i in y:
z = {}
z['amount'] = i.amount
z['currency'] = i.currency
ret = ret + [z]
if ret:
res['сумма'] = ret
else:
status = 0
returning = {}
if status == 1:
returning['status'] = 'успех'
else:
returning['status'] = 'не успех'
returning['entities'] = res
return returning