Chinese semantic role labeling using CRFs and SVMs

There is a widely held belief in the NLP and computational linguistics communities that identifying and defining roles of predicate arguments in a sentence has a lot of potential for and is a significant step toward improving important applications such as document retrieval, machine translation, question answering and information extraction. In this paper, we present an semantic role labeling (SRL) system for Chinese that exploits many aspects of the rich features of the languages. Finally, we compare system based on CRFs and SVMs. The experiment yields a global SRL FB1 score of 92.89%.