%0 Journal Article
%T Analysis and study on latent semantic index retrieval model<br>隐含语义索引模型的分析与研究
%A WANG Chun-hong
%A ZHANG Min
%A <br>王春红
%A 张敏
%J 计算机应用
%D 2007
%I 
%X Latent Semantic Index (LSI) retrieval model was designed by expanding Vector Space Model. Based on the profound research of Vector Space Model, LSI mapped documents and terms vectors into a lower dimensional space by Singular Value Decomposition (SVD), so that the inherent vagueness associated with a retrieval process based on keyword sets was considerably reduced and semantic association among the documents was highlighted consequently. Theoretic analyses show that LSI can improve retrieval performance significantly.
%K Vector Space Model
%K Latent semantic index
%K information retrieval<br>向量空间模型
%K 隐含语义索引
%K 信息检索
%K 隐含语义索引模型
%K 分析证明
%K 研究
%K retrieval
%K model
%K index
%K semantic
%K latent
%K study
%K 检索效果
%K 表达能力
%K 语义内容
%K 理论
%K 模糊度
%K 消减
%K 低维空间
%K 向量投影
%K 文档
%K 奇异值分解
%K 设计
%K 扩展
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=14E16CBD2743051279B50A98BA1FCC2C&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=94C357A881DFC066&sid=2F8F471CEC23CD85&eid=9A3A88A3A0B8496C&journal_id=1001-9081&journal_name=计算机应用&referenced_num=3&reference_num=6