def __iter__(self):
for filename in self.file_list[0:1]:
sent_file = os.path.join(self.annotation_dir, filename)
with open(sent_file) as file:
lc = LoopTimer(update_after=100)
lastid = None
for line in file:
if self.print_status:
lc.update("Posbigram Sent Stream")
data = json.loads(line)
xml = data['annotation']
id = data['id']
if lastid != id:
para_num = 0
else:
para_num += 1
lastid = id
token_list = mf.xml2words(xml)
pos_list = mf.xml2pos(xml)
for i in range(0, len(token_list)):
token_cleaned, pos_cleaned = utils.posFilterString(token_list[i], pos_list[i])
if len(token_cleaned) > 0:
yield id, para_num, utils.makeBigrams(pos_cleaned)
def __iter__(self):
for filename in self.file_list[0:1]:
sent_file = os.path.join(self.annotation_dir, filename)
with open(sent_file) as file:
lc = LoopTimer(update_after=100)
abs_list = []
lastid = None
for line in file:
if self.print_status:
lc.update("Lemma Doc Stream")
data = json.loads(line)
doc_id = data['id']
xml = data['annotation']
if lastid != doc_id and len(abs_list) > 0:
# Yield Stuff
yield lastid, abs_list
abs_list = []
lastid = doc_id
token_list = mf.xml2lemmas(xml)
pos_list = mf.xml2pos(xml)
for i in range(0, len(token_list)):
token_cleaned, pos_cleaned = utils.posFilterString(token_list[i], pos_list[i])
if len(token_cleaned) > 0:
for j in range(0, len(token_cleaned)):
abs_list.append(token_cleaned[j])
if len(abs_list) > 0:
# Yield Stuff
yield lastid, abs_list
def __iter__(self):
for filename in self.file_list[0:1]:
sent_file = os.path.join(self.annotation_dir, filename)
with open(sent_file) as file:
lc = LoopTimer(update_after=100)
for line in file:
if self.print_status:
lc.update("Lemma Para Stream")
data = json.loads(line)
doc_id = data['id']
para_id = data['paragraphID']
xml = data['annotation']
token_list = mf.xml2lemmas(xml)
pos_list = mf.xml2pos(xml)
para_list = []
for i in range(0, len(token_list)):
token_cleaned, pos_cleaned = utils.posFilterString(token_list[i], pos_list[i])
if len(token_cleaned) > 0:
for j in range(0, len(token_cleaned)):
para_list.append(token_cleaned[j])
yield doc_id, para_id, para_list
def nlp_to_doc_token(annotation, token_type, clean=True, lower=False, bigrams=False, dictionary=None):
sentences = annotation['sentences']
abs_list = list()
for sentence in sentences:
pos_list = list()
token_list = list()
for token in sentence['tokens']:
pos_list.append(token['pos'])
# oText = token['originalText']
if lower:
token_list.append(token[token_type].lower())
else:
token_list.append(token[token_type])
if clean:
token_list, pos_cleaned = utils.posFilterString(token_list, pos_list)
if dictionary is not None:
token_list = [word for word in token_list if word in dictionary.token2id]
if bigrams:
token_list = utils.makeBigrams(token_list)
abs_list.extend(token_list)
return abs_list
