语义角色标注中有效的识别论元算法研究  

Research of effective argument identification algorithm in semantic role labeling

作　　者：丁金涛[1] 周国栋[1] 王红玲[1] 朱巧明[1] 

机构地区：[1]苏州大学计算机科学与技术学院

出　　处：《计算机工程与应用》2008年第18期153-156,共4页Computer Engineering and Applications

基　　金：国家高技术研究发展计划(863)(the National High-Tech Research and Development Plan of China under Grant No.2006AA01Z147);国家自然科学基金(the National Natural Science Foundation of China under Grant No.60673041);高等院校博士学科点专项科研基金(the China Specialized Research Fund for the Doctoral Program of Higher Education under Grant No.20060285008)

摘　　要：语义角色标注中论元识别的结果对论元分类任务起着很重要的作用。以句法成分的中心词为依据,对论元识别算法进行研究,在训练集上识别出了98.78%的论元,在测试集识别出了97.17%的论元,并大大减少了不承担角色的训练样例。在此基础上以句法成分为标注单元,在自动句法分析上抽取和组合有用的特征,用支持向量机的方法进行学习分类,在测试集上获得77.84%的F1值。此结果是目前报告的基于单一句法分析的最好结果之一。Argument identification plays an important role for argument classification task in semantic role labeling.According to the headwords of the constituents,this paper researches on argument identification algorithm.The experiment shows that 98.78% of arguments on train set and 97.17% on test set are identified.At the same time,most of NULL arguments are pruned.The existed features are re-combined and optimized to capture more useful information.A SVM classifier is used in the semantic role labeling system,which took syntactic constituents as labeled units,The F1-score of SRL on test set achieves 77.84%.So far as it is known,it is one of the best result based on single syntactic parser in literatures.

关 键 词：语义角色标注 论元识别 支持向量机 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]