new_contents: 
  
  from nltk.parse import DependencyGraph
  from InformationExtractor import InformationExtractor
  
  
  def dadegan_text(conll_file='resources/train.conll'):
  	text = open(conll_file).read()
  	return text.replace('‌‌','‌').replace('\t‌','\t').replace('‌\t','\t').replace('\t ','\t').replace(' \t','\t').replace('\r', '').replace('\u2029', '‌')
  
  extractor = InformationExtractor()
  output = open('informations.txt', 'w')
  for sentence in map(DependencyGraph, [item for item in dadegan_text().replace(' ', '_').split('\n\n') if item.strip()]):
  	print('\n', '*', *[node['word'] for node in sentence.nodelist if node['word']], file=output)
  	for information in extractor.extract(sentence):
  		print(*information, sep=' - ', file=output)
  
  
subject: add dadegan inforamtion extractor