An improved algorithm for weighting keywords in web documents  

作　　者：孙双 贺樑 杨静 顾君忠 

机构地区：[1]Institute of Computer Applications, East China Normal University, Shanghai 200062, P. R. China

出　　处：《Journal of Shanghai University(English Edition)》2008年第3期235-239,共5页上海大学学报（英文版）

基　　金：Project supported by the Science Foundation of Shanghai Municipal Commission of Science and Technology （Grant No.055115001）

摘　　要：In this paper, an improved algorithm, web-based keyword weight algorithm （WKWA）, is presented to weight keywords in web documents. WKWA takes into account representation features of web documents and advantages of the TF＊IDF, TFC and ITC algorithms in order to make it more appropriate for web documents. Meanwhile, the presented algorithm is applied to improved vector space model （IVSM）. A real system has been implemented for calculating semantic similarities of web documents. Four experiments have been carried out. They are keyword weight calculation, feature item selection, semantic similarity calculation, and WKWA time performance. The results demonstrate accuracy of keyword weight, and semantic similarity is improved.In this paper, an improved algorithm, web-based keyword weight algorithm （WKWA）, is presented to weight keywords in web documents. WKWA takes into account representation features of web documents and advantages of the TF＊IDF, TFC and ITC algorithms in order to make it more appropriate for web documents. Meanwhile, the presented algorithm is applied to improved vector space model （IVSM）. A real system has been implemented for calculating semantic similarities of web documents. Four experiments have been carried out. They are keyword weight calculation, feature item selection, semantic similarity calculation, and WKWA time performance. The results demonstrate accuracy of keyword weight, and semantic similarity is improved.

关 键 词：improved vector space model （IVSM） representation feature feature item keyword weight semantic similarity 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]