Previous studies on discourse analysis have been quite successful in identifying what machine learning approaches and what features are more useful for automatic discourse segmentation and parsing (Soricut and Marcu, 2003;Subba and Eugenio, 2009;du-Verle and Prendinger, 2009).
all the proposed solutions suffer from at least one of the following two key limitations: first, they make strong independence assumptions on the structure and the labels of the resulting DT, and typically model the construction of the DT and the labeling of the relations separately; second, they apply a greedy, suboptimal algorithm to build the structure of the DT.