from nltk.corpus import reuters

from nltk import bigrams, trigrams

from collections import Counter, defaultdict

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

print(dict(model['today','the']))