A global generative model for chinese semantic role labeling

Abstract

The predicate and its semantic roles compose a unified entity that conveys the semantics of a given sentence. A standard pipeline of current approaches to semantic role labeling (SRL) is that for a given predicate in a sentence, we can extract features for each candidate argument and then perform the role classification through a classifier. However, this process totally ignores the integrality of the predicate and its semantic roles. To address this problem, we present a global generative model in which a novel concept called Predicate-Arguments-Coalition (PAC) is proposed to encode the relations among individual arguments. Owing to PAC, our model can effectively mine the inherent properties of predicates and obtain a globally consistent solution for SRL. We conduct experiments on the standard benchmarks: Chinese PropBank. Experimental results on a single syntactic tree show that our model outperforms the state-of-the-art methods.