1. A method for characterizing a class of a probabilistic classifier or clustering system that includes probabilistic model parameters, the method comprising: for each of a plurality of candidate words or word combinations, computing divergence of the class from other classes based on one or more probabilistic model parameters profiling the candidate word or word combination; and selecting one or more words or word combinations for characterizing the class as those candidate words or word combinations for which the class has substantial computed divergence from the other classes.