Note that there are some similarities between our two-stage semi-supervised learning approach and the semi-supervised learning method introduced by (Blitzer et al., 2006), which is an extension of the method described by (Ando and Zhang, 2005).
both methods use a two-stage approach; They first train generative models or auxiliary problems from unlabeled data, and then, they incorporate these trained models into a supervised learning algorithm as real valued features.