We develop a semi-supervised algorithm that extends self-training by estimating the labels of unlabeled data and then using those labels for retraining.
we use a graph-based algorithm to estimate the posterior probabilities of unlabeled data and develop a new CRF training to take the uncertainty of the estimated labels into account while optimizing the objective function.