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.
出处
《计算机工程与应用》
CSCD
北大核心
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)
关键词
语义角色标注
论元识别
支持向量机
semantic role labeling
argument identification
support vector machine