As we will demonstrate below, this approach admits an efficient training procedure that can find a global minimum, and, perhaps surprisingly, can systematically improve the accuracy of supervised training approaches for learning dependency parsers.
in this paper, we focus on semi-supervised language learning, where we can make use of both labeled and unlabeled data.