Bayesian methods have been applied to a number of segmentation tasks in natural language processing, including word segmentation, TSG learning, and learning machine translation rules, as a way of controlling the overfitting produced when Expectation Maximization would tend to prefer longer segments.
it is important to note that the Bayesian priors in most cases control the size and number of the clusters, but do not explicitly control the size of rules.