To train the model, we find a parameter vector θ that maximizes the regularized log marginal probability of the compatibility function being satisfied.
given training data D = where Note that c( z w ) = 1 when the entity list y = z w selected by z is compatible with the annotation; otherwise, c( z w ) = 0.