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I am creating an n-gram model that will predict the next word after an n-gram (probably unigram, bigram and trigram) as coursework.

I have seen lots of explanations about HOW to deal with zero probabilities for when an n-gram within the test data was not found in the training data. I understand how 'add-one' smoothing and some other techniques work.

However, I can find nothing about WHY we need to take actions such as these.

For instance, if the test data has "Peace begins with a Smile" and this was not in the training data, so when I supply the model with "Peace begins with a", it will not come up with "Smile" end word. It may provide others or none. If there are none or they have a low probability, then I would supply the shorter n-gram of "begins with a" and see what words and probabilities that provides. If that fails, then "with a" and so on.

I suspect I'm missing something but can't see what.

Please can you help?

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  • $\begingroup$ What you call a low probability is not a problem as such, though it is an indicator that your prediction is likely to be wrong. The problem is what you call none: the shorter phrase does not appear in your training data, and in the worst case perhaps the preceding word does not appear at all in your training data. $\endgroup$
    – Henry
    Aug 4, 2019 at 9:39
  • $\begingroup$ Thanks @Henry. I understand that, but I’m not sure how any of the smoothing techniques help. In the end, it simply isn’t in the training data so can’t be reliably predicted. $\endgroup$
    – Chris
    Aug 4, 2019 at 13:44

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What you are saying is called interpolation or back-off which is another technique that handles zero probability of n-grams.

See and check this: http://www.cs.cornell.edu/courses/cs4740/2014sp/lectures/smoothing+backoff.pdf

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