I will present a novel approach to semi-supervised learning that employs a method which we refer to as structural learning (aka multi-task learning). The idea is to learn predictive structures from many auxiliary problems that are created from the unlabeled data (and are related to the target problem), and then transfer the learned structure to the supervised target problem.