The Bayesian framework is well-matched to our problem since our inductive biases -those derived from universal grammar principles, weak supervision, and estimations based on unannotated data -can be encoded as priors, and we can use Markov chain Monte Carlo (MCMC) inference procedures to automatically blend these biases with unannotated text that reflects the way language is actually used "in the wild".
we learn context information based on statistics in the data like CCM, but have the advantage of additional, a priori biases.