基于浅层句法分析的中文语义角色标注研究

Research on Chinese Semantic Role Labeling Based on Shallow Parsing

语义角色标注是获取语义信息的一种重要手段。许多现有的语义角色标注都是在完全句法分析的基础上进行的,但由于现阶段中文完全句法分析器性能比较低,基于自动完全句法分析的中文语义角色标注效果并不理想。因此该文将中文语义角色标注建立在了浅层句法分析的基础上。在句法分析阶段,利用构词法获得词语的"伪中心语素"特征,有效缓解了词语级别的数据稀疏问题,从而提高了句法分析的性能,F值达到了0.93。在角色标注阶段,利用构词法获得了目标动词的语素特征,细粒度地描述了动词本身的结构,从而为角色标注提供了更多的信息。此外,该文还提出了句子的"粗框架"特征,有效模拟了基于完全句法分析的角色标注中的子类框架信息。该文所实现的角色标注系统的F值达到了0.74,比前人的工作(0.71)有较为显著的提升,从而证明了该文的方法是有效的。

Semantic role labeling（SRL）is an important way to get semantic information.Many existing systems forSRL make use of full syntactic parses.But due to the low performance of the existing Chinese parser,the performance of labeling based on the full syntactic parses is still not satisfactory.This paper realizes SRL methods based on shallow parsing.In shallow parsing stage,this paper makes use of word formation to get fake head morpheme information,which alleviates the problem of data sparseness,and imporves the performance of the parser with the F-score up to 0.93.In the stage of semantic role labeling,this paper applies word formation to get morpheme information of the target verb,which describes the structure of word in fine granualrity,and provides more information for semantic role labeling.In addition,this paper also proposes a coarse frame feature as an approximation of the sub-categorization information existing full syntactic parsing.F-score of this semantic role labeling system has reached 0.74,a significant improvements over the best reported SRL performance（0.71） in the literature.