基于中文宾州树库的浅层语义分析  

Shallow semantic parsing based on Chinese Penn Treebank

作　　者：连乐新[1] 胡仁龙[1] 杨翠丽[1] 袁春风[1] 

机构地区：[1]南京大学计算机科学与技术系计算机软件新技术国家重点实验室,南京210093

出　　处：《计算机应用研究》2008年第3期674-676,680,共4页Application Research of Computers

基　　金：国家"863"计划资助项目(2002AA117010-10);国家自然科学基金资助项目(60673043);"十五"攻关教育部科技基础条件平台建设项目

摘　　要：采用支持向量机的机器学习方法,以中文宾州树库为基础,对中文文本进行了部分语义角色标注实验。选取了主语、宾语、间接宾语、时间和地点这五种主要的语义角色,以中文PropBank 5.0中的前1 652个句子作为实验的训练集和测试集,选择路径、短语类型、谓词、头词、头词词性等八个属性作为分类特征,采用两阶段分类方法,在测试集上得到的总体语义角色标注的准确率和召回率分别为89.73%和91.26%。实验结果表明该方法对中文浅层语义分析工作是有效的。This paper presented an experiment on semantic role labeling by using SVM. This experiment was based on Chinese PropBank 5.0, which consisted of 1 652 sentences. The role-labeling set of this experiment included subject, object, !ndirect object, time and location. It used two-phase classification method with eight features, including path, phrase type, etc. For the small scaled training set, the experiment on testing set could reach the accuracy of 89.73% and the recall of 91.26% for semantic role labeling. Results highlight the effectiveness and efficiency of proposed approach for shallow semantic parsing of Chinese.

关 键 词：支持向量机 语义角色标注 中文宾州树库 中文PropBank 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]