accounting for unseen words and other oddities when we calculated the preceding probabilitieswe actually cheated ourselves we were not calculating the real probabilitiesbut only rough approximations by means of the fractions we assumed that the training corpus would tell us the whole truth about the real probabilities it did not corpus of only six tweets obviously cannot give us all the information about every tweet that has ever been written for examplethere certainly are tweets containing the word "text"it is just that we have never seen them apparentlyour approximation is very roughso we should account for that this is often done in practice with "add-one smoothingadd-one smoothing is sometimes also referred to as additive smoothing or laplace smoothing note that laplace smoothing has nothing to do with laplacian smoothingwhich is related to smoothing of polygon meshes if we do not smooth by one but by an adjustable parameter alpha greater than zeroit is called lidstone smoothing it is very simple techniquesimply adding one to all counts it has the underlying assumption that even if we have not seen given word in the whole corpusthere is still chance that our sample of tweets happened to not include that word sowith add-one smoothing we pretend that we have seen every occurrence once more than we actually did that means that instead of calculating the followingwe now calculatewhy do we add in the denominatorwe have to make sure that the end result is again probability thereforewe have to normalize the counts so that all probabilities sum up to one as in our current dataset awesomecan occur either zero or one timewe have two cases and indeedwe get as the total probability