Another type of method designed to make up for a lack of training data, is automatic labeled data expansion (Mihalcea and Moldovan, 1999;Agirre and Martinez, 2000) .
when using labeled data for semi-supervised learning, minimum expansion provides the best performance, and protects the system against sense bias.