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# code courtesy of
from nltk.corpus import reuters
from nltk import bigrams, trigrams
from collections import Counter, defaultdict
# Create a placeholder for model
model = defaultdict(lambda: defaultdict(lambda: 0))
# Count frequency of co-occurance
for sentence in reuters.sents():
for w1, w2, w3 in trigrams(sentence, pad_right=True, pad_left=True):
model[(w1, w2)][w3] += 1
# Let's transform the counts to probabilities
for w1_w2 in model:
total_count = float(sum(model[w1_w2].values()))
for w3 in model[w1_w2]:
model[w1_w2][w3] /= total_count
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