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I created an n-gram language model on the Penn Treebank using the following command:

ngram-count -text $trainfile -order 5 -lm $temp/templm.ptb -gt3min 1 -gt4min 1 -kndiscount -interpolate -unk

This code snippet was taken from Mikolov's rnnlm toolkit. I tried to check if the created ngram LM is valid, and I think it is not (or maybe I don't understand something). When I open the created ARPA file, I looked, for example, on all bigrams that start with the word "country":

-1.445136   country 's  -0.06955435
-0.91566    country </s>
-1.380222   country <unk>   -0.06098625
-2.46688    country N   -0.06098624
...
-1.756313   country with    -0.06098624
-2.699641   country without -0.06098625
-1.975222   country would   -0.1501413

and on the unigram

-3.751397   country -0.3271779

Now, as I understand (and probably I have a mistake somewhere), the number to the left of all bigrams are the log10 conditional probability: $\log_{10}(\Pr(\text{word|prev word=country}))$. In addition, the number to the right of the unigram "country" (which is -0.3271779) is log10 of the backoff. When I sum all the probabilities P(word|prev word=country) (without the backoff) I get: $$ \sum_{word\in W} \Pr(\text{word|prev word=country})=0.64$$ The backoff number is $$ 10^{-0.3271779}=0.47078444$$ When I add this number to the sum I get 1.110339152, which is higher than 1. What do I miss? Shouldn't it be equal to one?

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When you have a bigram like "country with" you should be backing-off to the unigram "with" not the unigram "country". Additionally, the backoff weight is different for each bigram. You need to look at the number to the right of "country with" to see how much weight to take from the unigram probability for "with".

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I think "Backoff" term need to multipy lower-order probability. that is sum(10 ** (-0.3271779 + p)), where p is lower-order log probability of those n-gram not exists in train set, and finally sum up all of them.

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