old_contents: # -*- coding: UTF-8 -*-
  
  import nltk
  
  # Primeira execução: baixa os dados utilizados pela biblioteca
  # nltk.download()
  
  # EOS Detection
  
  txt = "Mr. Green killed Colonel Mustard in the study with the \
         candlestick. Mr. Green is not a very nice fellow."
  
  sentences = nltk.tokenize.sent_tokenize(txt)
  
  # Tokenization
  
  tokens = []
  for s in sentences:
      tokens.append(nltk.tokenize.word_tokenize(s))
  
  # POS Tagging
  
  pos_tagged_tokens = []
  for t in tokens:
      pos_tagged_tokens.append(nltk.pos_tag(t))
  
  # Chunking
  
  ne_chunk = []
  for chunk in nltk.ne_chunk_sents(pos_tagged_tokens):
      ne_chunk.append(chunk)
  
  for c in ne_chunk:
      print c
  
new_contents: # -*- coding: UTF-8 -*-
  
  import nltk
  
  # Baixa os dados utilizados pela biblioteca
  nltk.download('punkt')
  nltk.download('maxent_treebank_pos_tagger')
  nltk.download('maxent_ne_chunker')
  nltk.download('words')
  
  # EOS Detection
  
  txt = "Mr. Green killed Colonel Mustard in the study with the \
         candlestick. Mr. Green is not a very nice fellow."
  
  sentences = nltk.tokenize.sent_tokenize(txt)
  
  # Tokenization
  
  tokens = []
  for s in sentences:
      tokens.append(nltk.tokenize.word_tokenize(s))
  
  # POS Tagging
  
  pos_tagged_tokens = []
  for t in tokens:
      pos_tagged_tokens.append(nltk.pos_tag(t))
  
  # Chunking
  
  ne_chunk = []
  for chunk in nltk.ne_chunk_sents(pos_tagged_tokens):
      ne_chunk.append(chunk)
  
  for c in ne_chunk:
      print c
  
subject: Add nltk downloads