In last week's post, we covered the basics of conditional probabilities in language modeling. Let's now have another quick math lesson on joint probabilities.
A joint probability is useful when you're interested in the probability of an entire sequence of words. Here I can borrow an equation from Wikipedia:
The middle term is the true value of observing the word sequence w1 through wm. The right term is the approximation when you only have an order-n dataset. Here's an example using our Python library:
>>> import MicrosoftNgram >>> s = MicrosoftNgram.LookupService(model='urn:ngram:bing-body:jun09:2') >>> s.GetJointProbability('apple pie') -6.170992 >>> s.GetConditionalProbability('apple') + s.GetConditionalProbability('apple pie') -6.170992
As expected, the joint probability is the same as the product of the conditional probability [remember — those numbers are log values, and log(a*b)=log(a)+log(b)].
But there's a hitch — what happens if a particular sequence of words had never been observed? Even in a corpus as large as the web, this is going to happen. Should the joint probability be 0? Depending on your application, the answer might be yes -- but for most scenarios, if the word sequence was at all probable, an approximation would be preferable. This is where smoothing comes in to play.
A number of different smoothing techniques are used today, but in essence we assess what we call a backoff penalty for word combinations for which we've no data. P(wm|wm-n,...,wm-1) is approximated as P(wm|wm-n+1,...,wm-1) * BO(wm-n,...,wm-1), i.e. we back off to a lower-order n-gram and assess a penalty which is a function of the context.