Assuming that word probabilities are independent given a class, we have the NB classifier: The EM algorithm basically builds a classifier iteratively using NB and both the labeled source data and the unlabeled target data.
the major shortcoming is that the feature set, even with feature selection, may fit the labeled source data well but not the target data because the target data has no labels to be used in feature selection.