text: from nltk.corpus import stopwords
  from nltk.corpus import webtext
  from nltk.collocations import BigramCollocationFinder
  from nltk.metrics import BigramAssocMeasures
  set = set(stopwords.words('english'))
  stops_filter = lambda w: len(w) < 3 or w in set
  tokens=[t.lower() for t in webtext.words('grail.txt')]
  words=BigramCollocationFinder.from_words(tokens)
  words.apply_word_filter(stops_filter)
  print(words.nbest(BigramAssocMeasures.likelihood_ratio, 10))
  