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bigram_measures = nltk.collocations.BigramAssocMeasures()
trigram_measures = nltk.collocations.TrigramAssocMeasures()

# change this to read in your data
finder = BigramCollocationFinder.from_words(
   nltk.corpus.genesis.words('C:\CisionWikiSubject\data.txt'))

# only bigrams that appear 3+ times
finder.apply_freq_filter(2)

# return the 10 n-grams with the highest PMI
x = finder.nbest(bigram_measures.pmi, 20)