old_contents: 
  from __future__ import print_function
  import codecs
  from itertools import chain
  from hazm import *
  from baaz import DependencyTreeInformationExtractor
  
  
  hamshahri = HamshahriReader(root='corpora/hamshahri')
  persica = PersicaReader(csv_file='corpora/persica.csv')
  uninformatives = set(sum([line.split(' - ') for line in codecs.open('data/adverbs.dat', encoding='utf8').read().split('\n') if line.strip() and not line.startswith('#')], []))
  
  
  normalizer = Normalizer()
  tagger = POSTagger(model='resources/postagger.model')
  parser = TurboParser(tagger=tagger, lemmatizer=Lemmatizer(), model_file='resources/turboparser.model')
  extractor = DependencyTreeInformationExtractor()
  
  
  informations = codecs.open('resources/informations.txt', 'a+', encoding='utf8')
  processed_sentences = set([line.strip()[2:] for line in informations if line.startswith('#')])
  
  
  errors = codecs.open('resources/errors.txt', 'w', encoding='utf8')
  
  for text in chain(hamshahri.texts(), persica.texts()):
  	try:
  		sentences = [sentence for sentence in sent_tokenize(normalizer.normalize(text)) if len(sentence) > 15 and sentence not in processed_sentences]
  		if not sentences:
  			continue
  
  		for sentence, tree in zip(sentences, parser.parse_sents(map(word_tokenize, sentences))):
  			print('#', sentence, file=informations)
  
  			for information in extractor.extract(tree):
  				if information[1] not in uninformatives:
  					print(*information, sep=' - ', file=informations)
  			print(file=informations)
  
  	except Exception as error:
  		print(error, 'while prcoessing:', *sentences, sep='\n', file=errors)
  
new_contents: 
  from __future__ import print_function
  import codecs
  from itertools import chain
  from hazm import *
  from baaz import DependencyTreeInformationExtractor
  
  
  hamshahri = HamshahriReader(root='corpora/hamshahri')
  persica = PersicaReader(csv_file='corpora/persica.csv')
  uninformatives = set(sum([line.split(' - ') for line in codecs.open('data/adverbs.dat', encoding='utf8').read().split('\n') if line.strip() and not line.startswith('#')], []))
  
  
  normalizer = Normalizer()
  tagger = POSTagger(model='resources/postagger.model')
  # parser = TurboParser(tagger=tagger, lemmatizer=Lemmatizer(), model_file='resources/turboparser.model')
  parser = DependencyParser(tagger=tagger, lemmatizer=Lemmatizer())
  extractor = DependencyTreeInformationExtractor()
  
  
  informations = codecs.open('resources/informations.txt', 'a+', encoding='utf8')
  processed_sentences = set([line.strip()[2:] for line in informations if line.startswith('#')])
  
  
  errors = codecs.open('resources/errors.txt', 'w', encoding='utf8')
  
  for text in chain(hamshahri.texts(), persica.texts()):
  	try:
  		sentences = [sentence for sentence in sent_tokenize(normalizer.normalize(text)) if len(sentence) > 15 and sentence not in processed_sentences]
  		if not sentences:
  			continue
  
  		for sentence, tree in zip(sentences, parser.parse_sents(map(word_tokenize, sentences))):
  			print('#', sentence, file=informations)
  
  			for information in extractor.extract(tree):
  				if information[1] not in uninformatives:
  					print(*information, sep=' - ', file=informations)
  			print(file=informations)
  
  	except Exception as error:
  		print(error, 'while prcoessing:', *sentences, sep='\n', file=errors)
  
subject: use default DependencyParser instead of TurboParser