new_contents: from hazm import IOBTagger
  import codecs
  
  
  tagger = IOBTagger(patterns=[
  		'u:t=%X[0,1]',
  		'u:c=%X[0,2]',
  		'u:a=%X[0,3]'
  	])
  
  training = codecs.open('trainingSet.txt', 'r', encoding='utf8').read()
  
  sentences = []
  
  for i, raw_sentence in enumerate(training.split('\n\n')):
  	sentence = []
  	if len(raw_sentence) < 1:
  		continue
  	for word in raw_sentence.split('\n'):
  		features = word.split('\t')
  		sentence.append(tuple(features))
  	sentences.append(sentence)
  
  
  
  #THRESHOLD = int(len(sentences) * 0.9)
  tagger.train(sentences)
  tagger.save_model('informations.model')
  test = []
  for sentence in sentences:
  	test_sent = []
  	for token in sentence:
  		if len(token) < 5:
  			print(token)
  		test_sent.append(token[:-1])
  	test.append(test_sent)
  
  tagged_sents = tagger.tag_sents(test)
  corrects = 0
  total = 0
  for tagged, gold in zip(tagged_sents, sentences[:1]):
  	for tagged_token, gold_token in zip(tagged, gold):
  		if tagged_token[-1] != 'O':
  			print(tagged)
  		if tagged_token[-1] == gold_token[-1]:
  			corrects += 1
  		total += 1
  
  print(corrects / total)
  
  print(sentences[1])
  print()
  print(tagger.tag(test[1]))
  
subject: add learning based information extraction