Chinese Semantic Role Labeling with Dependency-Driven Constituent Parse Tree Structure

This paper explores a tree kernel-based method for nominal semantic role labeling (SRL). In particular, a new dependency-driven constituent parse tree (D-CPT) structure is proposed to better represent the dependency relations in a CPT-style structure, which employs dependency relation types instead of phrase labels in CPT. In this way, D-CPT not only keeps the dependency relationship information in the dependency parse tree (DPT) structure but also retains the basic structure of CPT. Moreover, several schemes are designed to extract various kinds of necessary information, such as the shortest path between the nominal predicate and the argument candidate, the support verb of the nominal predicate and the head argument modified by the argument candidate, from D-CPT . Evaluation on Chinese NomBank shows that our tree kernelbased method on D-CPT achieves comparable performance with the state-of-art feature-based ones. This indicates the effectiveness of the novel D-CPT structure for better representation of dependency relations in tree kernel-based methods. To our knowledge, this is the first research of tree kernel-based SRL on effectively exploring dependency relationship information, which achieves comparable performance with the state-of-the-art feature-based ones.