Many approaches have been used in DP, the majority of them using machine learning algorithms, such as probabilistic models<cite> (Soricut and Marcu, 2003)</cite> , SVMs (Reitter, 2003; duVerle and Prendinger, 2009; Hernault et al., 2010; Feng and Hirst, 2012) and dynamic conditional random field (Joty et al., 2012) . To obtain acceptable results, these approaches need plenty of labeled data. Given this fact, what can we do when there is not enough data to perform effective learning of DP, as in languages with little annotated data?