Combining constituent and dependency syntactic views for chinese semantic role labeling

This paper presents a novel feature-based semantic role labeling (SRL) method which uses both constituent and dependency syntactic views. Comparing to the traditional SRL method relying on only one syntactic view, the method has a much richer set of syntactic features. First we select several important constituent-based and dependency- based features from existing studies as basic features. Then, we propose a statistical method to select discriminative combined features which are composed by the basic features. SRL is achieved by using the SVM classifier with both the basic features and the combined features. Experimental results on Chinese Proposition Bank (CPB) show that the method outperforms the traditional constituent-based or dependency-based SRL methods.