中文名词性谓词语义角色标注  

Semantic Role Labeling in Chinese Language for Nominal Predicates

作　　者：李军辉[1,2] 周国栋[1,2] 朱巧明[1,2] 钱培德[1,2] 

机构地区：[1]苏州大学计算机科学与技术学院,江苏苏州215006 [2]江苏省计算机信息处理技术重点实验室,江苏苏州215006

出　　处：《软件学报》2011年第8期1725-1737,共13页Journal of Software

基　　金：国家自然科学基金(90920004;60873150;60970056);江苏省自然科学基金(BK2008160)

摘　　要：研究了中文名词性谓词的语义角色标注(semantic role labeling,简称SRL).在使用传统动词性谓词SRL相关特征的基础上,进一步提出了名词性谓词SRL相关的特征集.此外,探索了中文动词性谓词SRL对中文名词性谓词SRL的影响,并且联合谓词自动识别实现了全自动的中文名词性谓词SRL.在中文NomBank上的实验结果表明,中文动词性谓词的SRL合理使用能够大幅度提高中文名词性谓词的SRL性能;基于正确句法树和正确谓词识别,中文名词性谓词的SRL性能F1值达到了72.67,大大优于目前国内外的同类系统;基于自动句法树和自动谓词识别,性能F1值为55.14.This paper explores semantic role labeling （SRL） in the Chinese language for nominal predicates. In addition to the widely adopted features of verbal SRL, various nominal predicate-specific features are also explored. Moreover, the nominal SRL performance has been improved by properly integrating features that were derived from a state-of-the-art verbal SRL system. Finally, the paper explains in detail the nominal predicate recognition, which is essential in a fully automatic nominal SRL system. Evaluations on Chinese NomBank show that proper integration of a verbal SRL system significantly improves the performance of a nominal SRL. It also shows that this nominal SRL system achieves the performance of 72.67 in Fl-measure on golden parse trees and golden predicates, and outperforms the state-of-the-art nominal SRL systems in the Chinese language; however, the performance drops to 55.14 in Fl-measure on automatic parse trees and automatic predicates.

关 键 词：语义角色标注 名词性谓词相关特征 动词性语义角色标注特征 名词性谓词识别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]