def d2kb():
data = request.data
data = data.rstrip()
data = data.lstrip()
nif_post = NIFCollection.loads(data.decode('utf-8'), format='turtle')
mentions = []
for context in nif_post.contexts:
tweet = Tweet()
tweet.mentions = []
tweet.idTweet = context.uri
tweet.text = context.mention
try:
for phrase in context.phrases:
single_mention = (phrase.mention, phrase.beginIndex,
phrase.endIndex)
mentions.append(single_mention)
except:
print('no mentions')
if len(mentions) > 0:
if VERBOSE == 'yes':
print('\n\n:::: PREPROCESSING ::::\n\n')
start = time.time()
tweet = preprocessing_d2kb(tweet, mentions, VERBOSE)
end = time.time()
if VERBOSE == 'yes':
print('Running time: {}'.format(end - start))
if VERBOSE == 'yes':
print('\n\n:::: ENTITY SELECTION ::::\n\n')
start = time.time()
tweet.candidates = select_candidates(tweet, vocab2idx, TYPE, MAX,
BOOST, VERBOSE)
end = time.time()
if VERBOSE == 'yes':
print('Running time: {}'.format(end - start))
if VERBOSE == 'yes':
print('\n\n:::: DISAMBIGUATION ::::\n\n')
start = time.time()
tweet.entities = disambiguate_mentions(tweet, THRESHOLD, model,
device, vocab2idx, WS,
EXTRA, VERBOSE)
end = time.time()
if VERBOSE == 'yes':
print('Running time: {}'.format(end - start))
collection_name = "http://optic.ufsc.br/"
nif = annotation2nif(collection_name, tweet)
return nif
if MODE == 'a2kb':
continue
# D2KB Mode
else:
mentions = []
try:
# Get all the mentions present in the tweet
for phrase in context.phrases:
single_mention = (phrase.mention, phrase.beginIndex,
phrase.endIndex)
mentions.append(single_mention)
# If there is no mention in tweet, return the original tweet
except:
continue
if len(mentions) > 0:
tweet = preprocessing_d2kb(tweet, mentions, VERBOSE)
tweet.candidates = select_candidates(
tweet, vocab2idx, TYPE, MAX, BOOST, VERBOSE)
tweet.entities = disambiguate_mentions(
tweet, THRESHOLD, model, device, vocab2idx, WS, EXTRA,
VERBOSE)
# Create tweet semantically annotated, as nif, when there are mentions
# If not, just return the tweet as nif
collection_name = "http://optic.ufsc.br/"
nif = annotation2nif(collection_name, tweet)
with open('{}output/{}.ttl'.format(DATA_PATH, count),
'w') as output_file:
output_file.write(nif)
count += 1
