利用配价信息的语义角色标注

语义角色标注是一种浅层语义分析.现有的汉语语义分析方法和语义角色标注体系没有结合汉语的特点并有效刻画出汉语的本质特性,导致目前汉语语义角色标注性能与英语相比相差较大.在汉语中,配价结构可以较好地刻画汉语句子的句法结构和语义构成关系,因此,我们在考察配价语法的基础上适当修改了语义角色标注体系并将谓词本身的配价信息融入语义角色标注.实验结果表明,配价信息的使用能够较大幅度提高动名词性谓词的语义角色标注性能:基于正确句法树和正确谓词识别,动词性谓词的SRL性能F1值达到93.69％;名词性谓词的SRL性能F1值达到79.23％;均优于目前国内外的同类系统.

Semantic Role Labeling Utilizing Valence Information

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.