This motivates a fully-Bayesian approach, which, rather than committing to a particular set of parameter values as in an EM-based approach, integrates over all possible values of θ and, most importantly, allows the use of priors to favor the learning of the skewed distributions, through the use of the term P (θ|w) in the following equation: The question, then, is: which priors on θ would allow the acquisition of skewed distributions?
our approach imposes suffix-based constraints on the tagging of words that are unseen w.r.t.