def contains_companies(person: str, companies) -> bool:
if COMPANY_TYPES_RE.search(person):
# noinspection PyTypeChecker
for ant in nltk_re.get_companies(person): # type: CompanyAnnotation
if ant.name == ant.company_type or ant.name == ant.description:
continue
return True
for ant in companies:
# Solving this scenario: This Amendment to Employment Agreement ("Amendment") is entered into
# between Marsh Supermarkets, Inc. (the "Company"), and Don E. Marsh (the "Executive").
# because that is pretty common , even though it screws up this scenario
# "This is an agreement between John Smith and John Smith, LLC"
if person in ant.name:
return True
return False
def contains_companies(person:str, companies) -> bool:
if COMPANY_TYPES_RE.search(person):
for result in nltk_re.get_companies(person,
detail_type=True,
parse_name_abbr=True):
co_name, co_type, co_type_abbr, co_type_label, co_desc, co_abbr = result
if co_name == co_type or co_name == co_desc:
continue
return True
for co_name, co_type in companies:
# Solving this scenario: This Amendment to Employment Agreement ("Amendment") is entered into
# between Marsh Supermarkets, Inc. (the "Company"), and Don E. Marsh (the "Executive").
# because that is pretty common , even though it screws up this scenario
# "This is an agreement between John Smith and John Smith, LLC"
if person in co_name:
return True
return False
def get_company_annotations(
text: str,
strict: bool = False,
use_gnp: bool = False,
count_unique: bool = False,
name_upper: bool = False,
) -> Generator[CompanyAnnotation, None, None]:
"""
Find company names in text, optionally using the stricter article/prefix expression.
:param parse_name_abbr:
:param text:
:param strict:
:param use_gnp: use get_noun_phrases or NPExtractor
:param name_upper: return company name in upper case.
:param count_unique: return only unique companies - case insensitive.
:return:
"""
# skip if all text is in uppercase
if text == text.upper():
return
valid_punctuation = VALID_PUNCTUATION + ["(", ")"]
unique_companies = {} # type: Dict[Tuple[str, str], CompanyAnnotation]
if COMPANY_TYPES_RE.search(text):
# Iterate through sentences
for s_start, s_end, sentence in get_sentence_span_list(text):
# skip if whole phrase is in uppercase
if sentence == sentence.upper():
continue
if use_gnp:
phrases = list(
get_noun_phrases(sentence,
strict=strict,
valid_punctuation=valid_punctuation))
else:
phrases = list(np_extractor.get_np(sentence))
phrase_spans = PhrasePositionFinder.find_phrase_in_source_text(
sentence, phrases)
for phrase, p_start, p_end in phrase_spans:
if COMPANY_TYPES_RE.search(phrase):
# noinspection PyTypeChecker
for ant in nltk_re.get_companies(
phrase, use_sentence_splitter=False
): # type: CompanyAnnotation
if ant.name == ant.company_type or ant.name == ant.description:
continue
ant.coords = (ant.coords[0] + s_start + p_start,
ant.coords[1] + s_start + p_start)
if name_upper:
ant.name = ant.name.upper()
if count_unique:
unique_key = (ant.name.lower() if ant.name else
None, ant.company_type_abbr)
existing_result = unique_companies.get(unique_key)
if existing_result:
existing_result.counter += 1
else:
unique_companies[unique_key] = ant
else:
yield ant
if count_unique:
for company in unique_companies.values():
yield company
def get_companies(text: str,
strict: bool = False,
use_gnp: bool = False,
detail_type: bool = False,
count_unique: bool = False,
name_upper: bool = False,
parse_name_abbr: bool = False,
return_source: bool = False):
"""
Find company names in text, optionally using the stricter article/prefix expression.
:param text:
:param strict:
:param use_gnp: use get_noun_phrases or NPExtractor
:param detail_type: return detailed type (type, unified type, label) vs type only
:param name_upper: return company name in upper case.
:param count_unique: return only unique companies - case insensitive.
:param parse_name_abbr: return company abbreviated name if exists.
:param return_source:
:return:
"""
# skip if all text is in uppercase
if text == text.upper():
return
valid_punctuation = VALID_PUNCTUATION + ["(", ")"]
unique_companies = dict()
if COMPANY_TYPES_RE.search(text):
# Iterate through sentences
for sentence in get_sentence_list(text):
# skip if whole phrase is in uppercase
if sentence == sentence.upper():
continue
if use_gnp:
phrases = get_noun_phrases(sentence, strict=strict,
valid_punctuation=valid_punctuation)
else:
phrases = np_extractor.get_np(sentence)
for phrase in phrases:
if COMPANY_TYPES_RE.search(phrase):
for result in nltk_re.get_companies(phrase,
detail_type=True,
parse_name_abbr=True):
co_name, co_type, co_type_abbr, co_type_label, co_desc, co_abbr = result
if co_name == co_type or co_name == co_desc:
continue
if name_upper:
co_name = co_name.upper()
result = (co_name, co_type)
if detail_type:
result += (co_type_abbr, co_type_label, co_desc)
if parse_name_abbr:
result += (co_abbr,)
if return_source and not count_unique:
result = result + (sentence,)
if count_unique:
unique_key = (result[0].lower() if result[0] else None, co_type_abbr)
existing_result = unique_companies.get(unique_key)
if existing_result:
unique_companies[unique_key] = existing_result[:-1] + (existing_result[-1] + 1,)
else:
unique_companies[unique_key] = result + (1,)
else:
yield result
if count_unique:
for company in unique_companies.values():
yield company
