def predict(storyId, sentence):
tokenizedSentence = word_tokenize(sentence)
taggedToken = posTagger(sentence)
tagger = pycrfsuite.Tagger()
tagger.open("{}/{}.model".format(app.config["MODELS_DIR"],storyId))
predictedLabels = tagger.tag(sentToFeatures(taggedToken))
extractedEntities = extractEntities(zip(tokenizedSentence, predictedLabels))
return extractedEntities
def predict(storyId, sentence):
tokenizedSentence = word_tokenize(sentence, language='russian')
taggedToken = posTagger(sentence)
tagger = pycrfsuite.Tagger()
tagger.open("{}/{}.model".format(app.config["MODELS_DIR"], storyId))
predictedLabels = tagger.tag(sentToFeatures(taggedToken))
extractedEntities = extractEntities(zip(tokenizedSentence, predictedLabels))
return extractedEntities
def test_sequenceLabeler_predict(self):
print("test_sequenceLabeler_predict")
global id
global model_file
sentence = "I want to book a cab from Beijing"
tokenizedSentence = word_tokenize(sentence)
taggedToken = posTagger(sentence)
tagger = pycrfsuite.Tagger()
tagger.open(model_file)
predictedLabels = tagger.tag(
sequenceLabeler.sentToFeatures(taggedToken))
extractedEntities = sequenceLabeler.extractEntities(
zip(tokenizedSentence, predictedLabels))
print("extractedEntities:")
print(extractedEntities)