def get_employee_name(text, return_source=False):
definitions = list(get_definitions(text))
fake_person = False
found_employee = None
defined_employee_found = False
for d in definitions:
if d.lower() in TRIGGER_LIST_EMPLOYEE:
defined_employee_found = True
break
if defined_employee_found:
persons = list(get_persons(text))
companies = list(get_companies(text))
for p in persons:
person_is_a_company = False
for f in FALSE_PEOPLE:
if f in str(p).lower():
fake_person = True
if not fake_person:
for c in companies:
# persons and companies return slightly different values for same text
# so need to standardize to compare
if len(c) > 0:
if c[1] is not None and c[0] is not None:
company_full_string = str(
clean(c[0]) + clean(c[1]))
else:
company_full_string = str(clean(c[0]))
employee_full_string = str(clean(p))
# handle this- where get_companies picks up more surrounding text
# than get_persons: EMPLOYMENT AGREEMENT WHEREAS, Kensey Nash Corporation,
# a Delaware corporation (the “Company”) and Todd M. DeWitt
# (the “Executive”) entered into that certain Amended
# and Restated Employment Agreement,...
if (employee_full_string == company_full_string or
employee_full_string in company_full_string):
person_is_a_company = True
if not person_is_a_company and not fake_person:
found_employee = str(p)
# take first person found meeting our employee criteria
break
fake_person = False # reset for next person
if return_source:
return found_employee, text
else:
return found_employee
import lexnlp.nlp.en.segments.sentences as lex_sentences
import lexnlp.extract.en.dates as lex_dates
import lexnlp.extract.en.entities.nltk_maxent as lex_entities
direct_path = "/Users/brandon/Documents/Northwestern Courses/Winter 2019/CS+Law Innovation Lab/Orrick, Harrington, & Sutcliffe/Documents/Dish_Sample.txt"
with open(direct_path, 'r') as file:
brief = file.read()
processed_brief = lex_sentences.pre_process_document(brief)
sentences_brief = lex_sentences.get_sentence_list(processed_brief)
facts = []
for sentence in sentences_brief:
entities = lex_entities.get_persons(sentence)
for entity in entities:
facts.append((entity, sentence))
for fact in facts:
print("Question:\nWhy is {} relevant?\n\nAnswer:\n{}".format(
fact[0], fact[1]))
print("\n---------------\n")
'''
Question:
Why is Farmers Branch relevant?
Answer:
In 2009, DISH began a pilot program to test QPC, a new incentive-based system at several locations, including two of its eight offices in the North Texas region: Farmers Branch and North Richland Hills.
---------------
def _extract_variants_from_text(self, field, text: str, **kwargs):
persons = get_persons(text, return_source=False)
return list(persons) if persons else None
def extract_persons(self, text=None):
if not text:
text = self.text
return list(lex_entities.get_persons(text))