This paper proposes a novel method for sentence-level Chinese discourse tree building. The authors
constrcut a Chinese discourse annotated corpus in the framework of Rhetorical Structure Theory, and propose a
ranking-like SVM (SVM-R) model to automatically build the tree structure, which can capture the relative
associated strength among three consecutive text spans rather than only two adjacent spans. The experimental
results show that proposed SVM-R method significantly outperforms state-of-the-art methods in discourse parsing
accuracy. It is also demonstrated that the useful features for discourse tree building are consistent with Chinese
language characteristics.

Several methods are proposed to automatically identify coordination relation, which is the most widely distributed one among discourse relations. The authors exploit semantic similarity and structure similarity to compute the sentence similarity, using lexical similarity, maximum common substring calculation, maximum length matching around head word, special words strengthening. Three of the above methods are integrated, and the experiment achieves promising results.