As the CRF is a discriminative model, the lack of label information renders the CRF weights independent of p(x) and thus we cannot directly utilize the unlabeled data when training the CRF.
semi-supervised approaches to training discriminative models typically use the unlabeled data to construct a regularizer that is used to guide the learning process (Joachims, 1999;Lawrence and Jordan, 2005).