Semantic roles labeling is a kind of shallow semantic analysis.Existing Chinese semantic analysis methods and semantic roles labeling systems do not effectively characterize Chinese essential features

and it causes the currently larger difference between Chinese SRL systems and English SRL systems.Valence structures can better characterize syntactic structures and semantic constitution relations of Chinese sentences

so we appropriately modified the semantic roles labeling systems and incorporated the valence information of predicates into semantic roles labeling.Experimental results show that proper use of valence information significantly improves the performance of semantic roles labeling system:the verbal SRL approach achieves the performance of 93.69% in F1-measure and the nominal SRL approach achieves the performance of 79.23% in F1-measure on golden parse trees and golden predicates

and all outperform the state-of-the-art SRL systems.