Co-training is one of those methods, and has been extensively studied in NLP (Nigam and Ghani, 2000;Pierce and Cardie, 2001;Ng and Cardie, 2003;Mota and Grishman, 2008).
we showed that the performance of a name tagger based on co-training decays as the time gap between training data (seeds and unlabeled data) and test data increases (Mota and Grishman, 2008).