基于String Kernel和KPCA的负实例语法特征提取算法  

False instance grammatical feature extraction algorithm based on String Kernel and KPCA

作　　者：吕威[1,2] 林文昶[1] 姚正安[1] 李磊[1] 

机构地区：[1]中山大学软件研究所,广州510275 [2]北京师范大学珠海分校信息技术学院,广东珠海519085

出　　处：《计算机工程与应用》2009年第20期136-139,共4页Computer Engineering and Applications

基　　金：国家自然科学基金No.10171113;No.10471156~~

摘　　要：提出通过String Kernel方法把负实例语法数据库中的负实例转化成核矩阵,再用Kernel Principal Component Analysis(KPCA)对转换的核矩阵进行特征提取,进而可将原始负实例数据库按照这些特征分成多个容量较小的特征表。通过构造负实例特征索引表设计了一个分类器,待检查的句子通过此分类器被分配到某个负实例特征表里进行匹配搜索,而此特征表的特征属性数和记录数要远远小于原始负实例数据库中的相应数目,从而大大提高了检查的速度,同时不影响语法检查的精度。通过比较测试,可看出提出的方法在保证语法检查精确度的同时有更快的速度。This paper presents a method that translates false instance in grammatical database to kernel matrix through String Kernel,and uses KPCA to extract feature of the translated kernel matrix.We can separate the original false instance database into many small characteristic tables according to these extracted features,and design a classified machine by constructing false instance characteristic table.A new sentence is distributed to some characteristic table for matching of false instance through this classification machine.For characteristic table is much little than original false instance database,the running speed is enhanced very much without decreasing the accuracy of grammatical check.By compared with grammar inspection function of word,the new system demonstrates more quick speed while keeping the accuracy of grammatical check

关 键 词：STRING KERNEL 核主成分分析 负实例 特征提取 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]