def get_definitions(text,
return_sources=False,
decode_unicode=True) -> Generator:
"""
Find possible definitions in natural language.
:param decode_unicode:
:param return_sources:
:param text:
:return:
"""
for sentence in get_sentence_list(text):
result = set()
if decode_unicode:
sentence = unidecode.unidecode(sentence)
for item in TRIGGER_WORDS_PTN_RE.findall(sentence):
result.update(EXTRACT_PTN_RE.findall(item))
# case #2
result.update(PAREN_PTN_RE.findall(sentence))
for term in result:
if len(get_token_list(term)) <= MAX_TERM_TOKENS:
if return_sources:
yield (term, sentence)
else:
yield term
def get_copyright(text, return_sources=False) -> Generator:
"""
Find copyright in text.
:param text:
:param return_sources:
:return:
"""
# Iterate through sentences
if COPYRIGHT_PTN_RE.search(text):
for sentence in get_sentence_list(text):
for phrase in np_extractor.get_np(sentence):
cps = COPYRIGHT_PTN_RE.findall(phrase)
for cp_text, cp_sign, cp_date, cp_name in cps:
# TODO: catch in the general regex
if not cp_date:
cp_date_at_end = YEAR_PTN_RE.search(cp_name)
if cp_date_at_end:
cp_date = cp_date_at_end.group()
cp_name = re.sub(r'{}$'.format(cp_date), '', cp_name)
ret = (cp_sign.strip(),
cp_date.replace(' ', ''),
cp_name.strip(string.punctuation + string.whitespace))
if return_sources:
ret += (cp_text.strip(),)
yield ret
def get_constraints(text, strict=False) -> Generator:
"""
Find possible constraints in natural language.
:param text:
:param strict:
:return:
"""
# Iterate through all potential matches
for sentence in get_sentence_list(text):
for match in RE_CONSTRAINT.finditer(sentence.lower()):
# Get individual group matches
captures = match.capturesdict()
num_pre = len(captures["pre"])
num_post = len(captures["post"])
# Skip if strict and empty pre/post
if strict and (num_pre + num_post == 0):
continue
# Setup fields
constraint = captures.get("constraint").pop().lower()
pre = "".join(captures["pre"])
post = "".join(captures["post"])
if num_post == 0 and num_pre == 1:
combined = "{0} {1}".format(pre, constraint).lower().strip()
if combined in CONSTRAINT_PHRASES:
constraint = combined
# Append
yield (constraint, pre, post)
def process(input_directory, input_file, section_name):
#Speedup: bash runStanfordParserServer.sh, bash runSSTServer.sh
import sys
import pip
import spacy
import neuralcoref
import lexnlp.nlp.en.segments.sentences as lex_sentences
import question_generator as gen
import csv
import time
#Load
start_time = time.time()
with open(input_file, 'r') as file:
brief = file.read()
print("--- %s seconds to Load ---" % (time.time() - start_time))
#Preprocess
##start_time = time.time()
##brief = lex_sentences.pre_process_document(brief)
##print("--- %s seconds to LexNLP Preprocess---" % (time.time() - start_time))
start_time = time.time()
pronouns = spacy.load('en_core_web_sm')
neuralcoref.add_to_pipe(pronouns,greedyness=0.5,max_dist=100,blacklist=False)
neural = pronouns(brief)
brief = neural._.coref_resolved
print("--- %s seconds to Pronoun Fix ---" % (time.time() - start_time))
#Tokenize
start_time = time.time()
sentences = list(lex_sentences.get_sentence_list(brief))
questions = gen.QuestionGenerator()
print("--- %s seconds to Tokenize ---" % (time.time() - start_time))
def get_condition_annotations(text: str, strict=True) \
-> Generator[ConditionAnnotation, None, None]:
"""
Find possible conditions in natural language.
:param text:
:param strict:
:return:
"""
# Iterate through all potential matches
for sentence in get_sentence_list(text):
for match in RE_CONDITION.finditer(sentence):
# Get individual group matches
captures = match.capturesdict()
num_pre = len(captures["pre"])
num_post = len(captures["post"])
# Skip if strict and empty pre/post
if strict and (num_pre == 0 or num_post == 0):
continue
ant = ConditionAnnotation(
coords=match.span(),
condition=captures["condition"].pop().lower(),
pre=captures["pre"].pop(),
post=captures["post"].pop())
yield ant
def get_noun_phrases(text,
strict=False,
return_source=False,
window=3,
valid_punctuation=None) -> Generator:
"""
Get NNP phrases from text
"""
valid_punctuation = valid_punctuation or VALID_PUNCTUATION
# Iterate through sentences
for sentence in get_sentence_list(text):
# Tag sentence
sentence_pos = nltk.pos_tag(get_token_list(sentence))
# Iterate through chunks
nnps = []
last_nnp_pos = None
for i, chunk in enumerate(sentence_pos):
do_join = not strict and last_nnp_pos is not None and (
i - last_nnp_pos) < window
# Check label
if chunk[1] in ['NNP', 'NNPS']:
if do_join:
sep = "" if "(" in valid_punctuation and nnps[-1][
-1] == "(" else " "
nnps[-1] += sep + chunk[0]
else:
nnps.append(chunk[0])
last_nnp_pos = i
elif do_join:
if chunk[1] in ['CC'] or chunk[0] in valid_punctuation:
if chunk[0].lower() in ["or"]:
continue
nnps[-1] += (' ' if chunk[0].lower() in ['&', 'and', '(']
else '') + chunk[0]
last_nnp_pos = i
else:
last_nnp_pos = None
# Clean up names and yield
for nnp in nnps:
# Cleanup
nnp = nnp.strip()
if len(nnp) <= 2:
continue
if nnp.lower().endswith(' and'):
nnp = nnp[0:-4].strip()
elif nnp.endswith(' &'):
nnp = nnp[0:-2].strip()
nnp = strip_unicode_punctuation(nnp).strip(
string.punctuation).strip(string.whitespace)
if return_source:
yield nnp, sentence
else:
yield nnp
def get_geopolitical(text,
strict=False,
return_source=False,
window=2) -> Generator:
"""
Get GPEs from text
"""
# Iterate through sentences
for sentence in get_sentence_list(text):
# Tag sentence
sentence_pos = nltk.pos_tag(get_token_list(sentence))
# Iterate through chunks
gpes = []
last_gpe_pos = None
for i, chunk in enumerate(nltk.ne_chunk(sentence_pos)):
if isinstance(chunk, nltk.tree.Tree):
# Check label
if chunk.label() == 'GPE':
if not strict and last_gpe_pos is not None and (
i - last_gpe_pos) < window:
gpes[-1] += " " + " ".join([c[0] for c in chunk])
else:
gpes.append(" ".join([c[0] for c in chunk]))
last_gpe_pos = i
elif not strict and last_gpe_pos is not None and (
i - last_gpe_pos) < window:
if chunk[1] in ["NNP", "NNPS"]:
gpes[-1] += " " + chunk[0]
last_gpe_pos = i
elif chunk[1] in ["CC"] or chunk[0] in VALID_PUNCTUATION:
if chunk[0].lower() in ["or"]:
continue
gpes[-1] += (" " if chunk[0].lower() in ["&", "and"] else
"") + chunk[0]
last_gpe_pos = i
else:
last_gpe_pos = None
# Clean up names and yield
for gpe in gpes:
# Cleanup
gpe = gpe.strip()
if len(gpe) <= 2:
continue
if gpe.lower().endswith(" and"):
gpe = gpe[0:-4]
elif gpe.endswith(" &"):
gpe = gpe[0:-2]
gpe = strip_unicode_punctuation(gpe).strip(
string.punctuation).strip(string.whitespace)
if return_source:
yield gpe, sentence
else:
yield gpe
def get_trademarks(text) -> Generator:
"""
Find trademarks in text.
:param text:
:return:
"""
# Iterate through sentences
if TRADEMARK_PTN_RE.search(text):
for sentence in get_sentence_list(text):
for phrase in np_extractor.get_np(sentence):
tms = TRADEMARK_PTN_RE.findall(phrase)
for tm in tms:
yield tm
def process(input_directory, input_file, section_name):
#Speedup: bash runStanfordParserServer.sh, bash runSSTServer.sh
import sys
import pip
import spacy
import neuralcoref
import lexnlp.nlp.en.segments.sentences as lex_sentences
import question_generator as gen
import csv
import time
#Load
start_time = time.time()
with open(input_file, 'r') as file:
brief = file.read()
print("--- %s seconds to Load ---" % (time.time() - start_time))
#Preprocess
##start_time = time.time()
##brief = lex_sentences.pre_process_document(brief)
##print("--- %s seconds to LexNLP Preprocess---" % (time.time() - start_time))
start_time = time.time()
pronouns = spacy.load('en')
neuralcoref.add_to_pipe(pronouns,
greedyness=0.5,
max_dist=100,
blacklist=False)
neural = pronouns(brief)
brief = neural._.coref_resolved
print("--- %s seconds to Pronoun Fix ---" % (time.time() - start_time))
#Tokenize
start_time = time.time()
sentences = list(lex_sentences.get_sentence_list(brief))
questions = gen.QuestionGenerator()
print("--- %s seconds to Tokenize ---" % (time.time() - start_time))
#Print
start_time = time.time()
with open(input_directory + "/" + section_name + '.csv', 'w') as csvfile:
qawriter = csv.writer(csvfile)
qawriter.writerow(["Q", "A"])
for sentence in sentences:
flashcard = questions.generate_question(sentence)
if flashcard:
qawriter.writerow([flashcard[0]['Q'], flashcard[0]['A']])
print("--- %s seconds to Generate Questions ---" %
(time.time() - start_time))
def get_definitions(text,
return_sources=False,
decode_unicode=True) -> Generator:
"""
Find possible definitions in natural language in text.
The text will be split to sentences first.
:param decode_unicode:
:param return_sources: returns a tuple with the extracted term and the source sentence
:param text: the input text
:return:
"""
for sentence in get_sentence_list(text):
yield from get_definitions_in_sentence(sentence, return_sources,
decode_unicode)
def run_sentence_token_test(text, result, lowercase=False, stopword=False):
"""
Base test method to run against text with given results.
"""
# Get list from text
sentence_list = get_sentence_list(text)
# Check length first
assert len(sentence_list) == len(result)
# Check each sentence matches
for i in range(len(sentence_list)):
tokens = lexnlp_tests.benchmark_extraction_func(get_token_list,
sentence_list[i], lowercase=lowercase, stopword=stopword)
assert_list_equal(tokens, result[i])
def process(section_text, section_name):
#Speedup: bash runStanfordParserServer.sh, bash runSSTServer.sh
import sys
import pip
import spacy
import neuralcoref
import lexnlp.nlp.en.segments.sentences as lex_sentences
import question_generator as gen
import csv
import time
#Load
start_time = time.time()
brief = section_text
print("--- %s seconds to Load ---" % (time.time() - start_time))
start_time = time.time()
pronouns = spacy.load('en')
neuralcoref.add_to_pipe(pronouns,
greedyness=0.5,
max_dist=100,
blacklist=False)
neural = pronouns(brief)
brief = neural._.coref_resolved
print("--- %s seconds to Pronoun Fix ---" % (time.time() - start_time))
#Tokenize
start_time = time.time()
sentences = list(lex_sentences.get_sentence_list(brief))
questions = gen.QuestionGenerator()
print("--- %s seconds to Tokenize ---" % (time.time() - start_time))
#Print
start_time = time.time()
for sentence in sentences:
flashcard = questions.generate_question(sentence)
if flashcard:
partial = {
"question": flashcard[0]['Q'],
"answer": flashcard[0]['A']
}
result[section_name].append(partial)
print("--- %s seconds to Generate Questions ---" %
(time.time() - start_time))
def get_locations(text,
strict=False,
return_source=False,
window=2) -> Generator:
"""
Get locations from text using Stanford libraries.
:param window:
:param return_source:
:param strict:
:param text:
:return:
"""
# Iterate through sentences
for sentence in get_sentence_list(text):
# Tag sentence
sentence_pos = STANFORD_NER_TAGGER.tag(get_tokens_list(text))
# Iterate through chunks
locations = []
last_loc_pos = None
for i, token in enumerate(sentence_pos):
# Check label
if token[1] == 'LOCATION':
if not strict and last_loc_pos is not None and (
i - last_loc_pos) < window:
locations[-1] += (" " if not token[0].startswith("'") else
"") + token[0]
else:
locations.append(token[0])
last_loc_pos = i
else:
if token[0] in [".", ","]:
if not strict and last_loc_pos is not None and (
i - last_loc_pos) < window:
locations[-1] += (
" " if token[0] not in string.punctuation and
not token[0].startswith("'") else "") + token[0]
last_loc_pos = i
# Cleanup and yield
for location in locations:
location = strip_unicode_punctuation(location).strip(
string.punctuation).strip(string.whitespace)
if return_source:
yield location, sentence
else:
yield location
def get_persons(text,
strict=False,
return_source=False,
window=2) -> Generator:
"""
Get persons from text using Stanford libraries.
:param window:
:param return_source:
:param strict:
:param text:
:return:
"""
# Iterate through sentences
for sentence in get_sentence_list(text):
# Tag sentence
sentence_pos = STANFORD_NER_TAGGER.tag(get_tokens_list(text))
# Iterate through chunks
names = []
last_person_pos = None
for i, token in enumerate(sentence_pos):
# Check label
if token[1] == 'PERSON':
if not strict and last_person_pos is not None and (
i - last_person_pos) < window:
names[-1] += " " + token[0]
else:
names.append(token[0])
last_person_pos = i
else:
if token[0] in [".", ","]:
if not strict and last_person_pos is not None and (
i - last_person_pos) < window:
names[-1] += (" " if token[0] not in string.punctuation
else "") + token[0]
last_person_pos = i
# Cleanup and yield
for name in names:
name = strip_unicode_punctuation(name).strip(
string.punctuation).strip(string.whitespace)
if return_source:
yield name, sentence
else:
yield name
def get_conditions(text, strict=True) -> Generator:
"""
Find possible conditions in natural language.
:param text:
:param strict:
:return:
"""
# Iterate through all potential matches
for sentence in get_sentence_list(text):
for match in RE_CONDITION.finditer(sentence):
# Get individual group matches
captures = match.capturesdict()
num_pre = len(captures["pre"])
num_post = len(captures["post"])
# Skip if strict and empty pre/post
if strict and (num_pre == 0 or num_post == 0):
continue
# Otherwise, append
yield (captures["condition"].pop().lower(), captures["pre"].pop(),
captures["post"].pop())
def get_persons(text,
strict=False,
return_source=False,
window=2) -> Generator:
"""
Get names from text.
:param window:
:param return_source:
:param strict:
:param text:
:return:
"""
# Iterate through sentences
for sentence in get_sentence_list(text):
# Tag sentence
sentence_pos = nltk.pos_tag(get_token_list(sentence))
companies = list(get_company_annotations(text))
# Iterate through chunks
persons = []
last_person_pos = None
for i, chunk in enumerate(nltk.ne_chunk(sentence_pos)):
if type(chunk) == nltk.tree.Tree:
# Check label
if chunk.label() == 'PERSON':
if not strict and last_person_pos is not None and (
i - last_person_pos) < window:
persons[-1] += " " + " ".join([c[0] for c in chunk])
else:
persons.append(" ".join([c[0] for c in chunk]))
last_person_pos = i
elif not strict and last_person_pos is not None and (
i - last_person_pos) < window:
if chunk[1] in ["NNP", "NNPS"]:
persons[-1] += " " + chunk[0]
last_person_pos = i
elif chunk[1] in ["CC"] or chunk[0] in VALID_PUNCTUATION:
if chunk[0].lower() in ["or"]:
continue
persons[-1] += (" " if chunk[0].lower() in ["&", "and"]
else "") + chunk[0]
last_person_pos = i
else:
last_person_pos = None
# Cleanup and yield
for person in persons:
# Cleanup
person = person.strip()
if len(person) <= 2:
continue
if PERSONS_STOP_WORDS.search(person):
continue
person = strip_unicode_punctuation(person).strip(
string.punctuation).strip(string.whitespace)
if contains_companies(person, companies):
continue
if person.lower().endswith(" and"):
person = person[0:-4]
elif person.endswith(" &"):
person = person[0:-2]
if return_source:
yield person, sentence
else:
yield person
def get_companies(text: str,
use_article: bool = False,
detail_type: bool = False,
parse_name_abbr: bool = False,
return_source: bool = False) -> Generator:
"""
Find company names in text, optionally using the stricter article/prefix expression.
:param text:
:param use_article:
:param detail_type:
:param parse_name_abbr:
:param return_source:
:return:
"""
# Select regex
re_c = RE_ARTICLE_COMPANY if use_article else RE_COMPANY
# Iterate through sentences
for sentence in get_sentence_list(text):
for match in re_c.finditer(sentence):
captures = match.capturesdict()
company_type = captures["company_type_of"] or \
captures["company_type"] or \
captures["company_type_single"]
company_type = "".join(company_type).strip(
string.punctuation.replace(".", "") + string.whitespace)
company_type = company_type or None
company_name = "".join(captures["full_name"])
if company_type:
company_name = re.sub(r'%s$' % company_type, '', company_name)
company_name = company_name.strip(
string.punctuation.replace('&', '').replace(')', '') + string.whitespace)
company_name = re.sub(r'^\s*(?:and|&|of)\s+|\s+(?:and|&|of)\s*$', '',
company_name, re.IGNORECASE)
if not company_name:
continue
# f.e., a Delaware company
if company_name.lower().startswith('a ') or captures.get('article') == ['a']:
continue
company_description = captures["company_description_of"] or \
captures["company_description_and"] or \
captures["company_description"] or \
captures["company_description_single"]
company_description = "".join(company_description).strip(
string.punctuation + string.whitespace)
# catch ABC & Company LLC case
if company_description.lower() == 'company' and \
('& company' in company_name.lower() or 'and company' in company_name.lower()):
company_description = None
company_description = company_description or None
if company_description:
company_name = re.sub(r'[\s,]%s$' % company_description, '', company_name)
if not company_name or \
ARTICLE_RE.fullmatch(company_name) or \
re.match(r'.+?\s(?:of|in)$', company_name.lower()):
continue
if company_name in COMPANY_DESCRIPTIONS:
continue
abbr_name = "".join(captures["abbr_name"]) or None
ret = (company_name,
company_type)
if detail_type:
ret += (COMPANY_TYPES[company_type.lower()]['abbr'] if company_type else None,
COMPANY_TYPES[company_type.lower()]['label'] if company_type else None)
ret += (company_description,)
if parse_name_abbr:
ret += (abbr_name,)
if return_source:
ret += (sentence,)
# no args: = [company_name, company_type, company_description]
# detail_type: + [company_type_abbr, company_type_label]
# parse_name_abbr: + [abbr_name]
# return_source: + [source]
yield ret
def process_fields(doc: LeaseDocument, doc_text: str, task: ExtendedTask):
sentences = get_sentence_list(doc_text)
# fields = detect_fields(sentences, groups=('address',))
fields = detect_fields(sentences)
doc.address = fields.get('address')
if not doc.address:
doc.address = detect_address_default(doc_text, sentences)
# term
doc.commencement_date = fields.get('commencement_date')
doc.expiration_date = fields.get('expiration_date')
term_tuple = fields.get('term')
if term_tuple:
term = timedelta(days=term_tuple[2])
if doc.commencement_date and not doc.expiration_date:
doc.expiration_date = doc.commencement_date + term
elif not doc.commencement_date and doc.expiration_date:
doc.commencement_date = doc.expiration_date - term
if doc.commencement_date \
and doc.expiration_date \
and doc.commencement_date >= doc.expiration_date:
doc.expiration_date = None
# lease type
pay_taxes = int(fields.get('pay_taxes') or False)
pay_costs = int(fields.get('pay_costs') or False)
pay_insurance = int(fields.get('pay_insurance') or False)
lt = pay_taxes + pay_costs + pay_insurance
if lt == 3:
doc.lease_type = 'triple-net'
elif lt == 2:
doc.lease_type = 'double-net'
elif lt == 1:
doc.lease_type = 'single-net'
else:
doc.lease_type = 'gross'
# property type
property_types = list(fields.get('property_types__set') or set())
property_types.sort()
doc.property_type = '; '.join(property_types)
# permitted use
doc.permitted_uses = fields.get('permitted_use')
# prohibited use
doc.prohibited_uses = ProcessLeaseDocuments.ordered_list_without_repetitions(
fields.get('prohibited_use__list'))
renew_duration_tuple = fields.get('renew_non_renew_notice')
if renew_duration_tuple:
doc.renew_non_renew_notice_duration = timedelta(days=renew_duration_tuple[2])
auto_renew = fields.get('auto_renew')
if auto_renew is not None:
doc.auto_renew = auto_renew
area_square_feet_list = fields.get('area_square_feet__list')
if area_square_feet_list:
doc.area_size_sq_ft = area_square_feet_list[0]
doc.alterations_allowed = ProcessLeaseDocuments.ordered_list_without_repetitions(
fields.get('alterations_allowed__list'))
security_deposit = fields.get('security_deposit__set')
if security_deposit:
doc.security_deposit = max(security_deposit)
doc.rent_due_frequency = fields.get('rent_due_frequency')
mean_rent_per_month = fields.get('mean_rent_per_month__set')
if mean_rent_per_month:
doc.mean_rent_per_month = max(mean_rent_per_month)
def process_document(document):
doc_words = []
for sentence in get_sentence_list(document):
doc_words.extend(process_sentence(sentence))
return doc_words
def process_fields(doc: LeaseDocument, doc_text: str, task: ExtendedTask):
sentences = get_sentence_list(doc_text)
# fields = detect_fields(sentences, groups=('address',))
fields = detect_fields(sentences)
doc.address = fields.get('address')
if not doc.address:
doc.address = detect_address_default(doc_text, sentences)
if doc.address:
g = geocoder.google(doc.address)
if g.ok:
doc.address_latitude = g.lat
doc.address_longitude = g.lng
doc.address_country = g.country_long
doc.address_state_province = g.province_long
elif g.status and 'ZERO' in g.status:
# Google does not know such address - probably we detected it wrong.
doc.address = None
doc.address_state_province = None
doc.address_country = None
doc.address_longitude = None
doc.address_latitude = None
else:
task.log_warn(
'Google did not return geocode info for: {0}\nResponse: {1}'.format(doc.address,
g))
# return
# term
doc.commencement_date = fields.get('commencement_date')
doc.expiration_date = fields.get('expiration_date')
term_tuple = fields.get('term')
if term_tuple:
term = timedelta(days=term_tuple[2])
if doc.commencement_date and not doc.expiration_date:
doc.expiration_date = doc.commencement_date + term
elif not doc.commencement_date and doc.expiration_date:
doc.commencement_date = doc.expiration_date - term
if doc.commencement_date \
and doc.expiration_date \
and doc.commencement_date >= doc.expiration_date:
doc.expiration_date = None
# lease type
pay_taxes = int(fields.get('pay_taxes') or False)
pay_costs = int(fields.get('pay_costs') or False)
pay_insurance = int(fields.get('pay_insurance') or False)
lt = pay_taxes + pay_costs + pay_insurance
if lt == 3:
doc.lease_type = 'triple-net'
elif lt == 2:
doc.lease_type = 'double-net'
elif lt == 1:
doc.lease_type = 'single-net'
else:
doc.lease_type = 'gross'
# property type
property_types = list(fields.get('property_types__set') or set())
property_types.sort()
doc.property_type = '; '.join(property_types)
# permitted use
doc.permitted_uses = fields.get('permitted_use')
# prohibited use
doc.prohibited_uses = ProcessLeaseDocuments.ordered_list_without_repetitions(
fields.get('prohibited_use__list'))
renew_duration_tuple = fields.get('renew_non_renew_notice')
if renew_duration_tuple:
doc.renew_non_renew_notice_duration = timedelta(days=renew_duration_tuple[2])
auto_renew = fields.get('auto_renew')
if auto_renew is not None:
doc.auto_renew = auto_renew
area_square_feet_list = fields.get('area_square_feet__list')
if area_square_feet_list:
doc.area_size_sq_ft = area_square_feet_list[0]
doc.alterations_allowed = ProcessLeaseDocuments.ordered_list_without_repetitions(
fields.get('alterations_allowed__list'))
security_deposit = fields.get('security_deposit__set')
if security_deposit:
doc.security_deposit = max(security_deposit)
doc.rent_due_frequency = fields.get('rent_due_frequency')
mean_rent_per_month = fields.get('mean_rent_per_month__set')
if mean_rent_per_month:
doc.mean_rent_per_month = max(mean_rent_per_month)
def test_sentence_segmenter_empty():
"""
Test basic sentence segmentation.
"""
_ = get_sentence_list("")
import sys
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.
else:
text_content = ''
except Exception as e:
print(("error in content extraction", e))
continue
# skip if empty
if text_content is None:
continue
if len(text_content.strip()) == 0:
continue
try:
# build word2vec sentence list and doc2vec content simultaneously
doc_stems = []
for sentence in get_sentence_list(text_content):
sentence_stems = [
s for s in get_stem_list(
sentence, stopword=True, lowercase=True)
if s.isalpha()
]
doc_stems.extend(sentence_stems)
sentences.append(sentence_stems)
documents.append(
gensim.models.doc2vec.TaggedDocument(
doc_stems,
["{0}".format(court_tar_member.name)]))
except Exception as e:
print(e)
# word2vec models
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
##brief = lex_sentences.pre_process_document(brief)
##print("--- %s seconds to LexNLP Preprocess---" % (time.time() - start_time))
start_time = time.time()
pronouns = spacy.load('en')
neuralcoref.add_to_pipe(pronouns,
greedyness=0.5,
max_dist=100,
blacklist=False)
neural = pronouns(brief)
brief = neural._.coref_resolved
print("--- %s seconds to Pronoun Fix ---" % (time.time() - start_time))
#Tokenize
start_time = time.time()
sentences = list(lex_sentences.get_sentence_list(brief))
questions = gen.QuestionGenerator()
print("--- %s seconds to Tokenize ---" % (time.time() - start_time))
#Print
start_time = time.time()
with open(
'/Users/brandon/Documents/Northwestern Courses/Winter 2019/CS+Law Innovation Lab/Orrick, Harrington, & Sutcliffe/Documents/ex.csv',
'w') as csvfile:
qawriter = csv.writer(csvfile)
qawriter.writerow(["Q", "A"])
for sentence in sentences:
flashcard = questions.generate_question(sentence)
if flashcard:
qawriter.writerow([flashcard[0]['Q'], flashcard[0]['A']])
print("--- %s seconds to Generate csv ---" % (time.time() - start_time))
def get_organizations(text,
strict=False,
return_source=False,
window=2) -> Generator:
"""
Get organizations from text.
:param window:
:param return_source:
:param strict:
:param text:
:return:
"""
# Iterate through sentences
for sentence in get_sentence_list(text):
# Tag sentence
sentence_pos = nltk.pos_tag(get_token_list(sentence))
# Iterate through chunks
organizations = []
last_org_pos = None
for i, chunk in enumerate(nltk.ne_chunk(sentence_pos)):
if type(chunk) == nltk.tree.Tree:
# Check label
if chunk.label() in ['ORGANIZATION']:
if not strict and last_org_pos is not None and (
i - last_org_pos) < window:
organizations[-1] += " " + " ".join(
[c[0] for c in chunk])
else:
organizations.append(" ".join([c[0] for c in chunk]))
last_org_pos = i
elif not strict and last_org_pos is not None and (
i - last_org_pos) < window:
if chunk[1] in ["NNP", "NNPS"]:
organizations[-1] += " " + chunk[0]
last_org_pos = i
elif chunk[1] in ["CC"] or chunk[0] in VALID_PUNCTUATION:
if chunk[0].lower() in ["or"]:
continue
organizations[-1] += (" " if chunk[0].lower()
in ["&", "and"] else "") + chunk[0]
last_org_pos = i
else:
last_org_pos = None
for org in organizations:
# Cleanup
org = org.strip()
if len(org) <= 2:
continue
if org.lower().endswith(" and"):
org = org[0:-4]
elif org.endswith(" &"):
org = org[0:-2]
org = strip_unicode_punctuation(org).strip(
string.punctuation).strip(string.whitespace)
if return_source:
yield org, sentence
else:
yield org
def get_sentences(self, text=None):
if not text:
text = self.text
return list(lex_sentences.get_sentence_list(text))
