Optimising the Heuristics in Latent Semantic Indexing for Effective Information Retrieval

被引：5
|
作者：
Srinivas, S. [1 ]
AswaniKumar, Ch [2 ]
机构：
[1] Deemed Univ, Sch Sci & Human, Vellore Inst Technol, Vellore, Tamil Nadu, India
[2] Deemed Univ, Sch Comp Sci, Vellore Inst Technol, Vellore, Tamil Nadu, India
来源：
JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT | 2006年 / 5卷 / 02期
关键词：
Information retrieval; Latent Semantic Indexing; rank approximation; term weighting; vector space method;
D O I：
10.1142/S0219649206001359
中图分类号：
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号：
1205 ; 120501 ;
摘要：
Latent Semantic Indexing (LSI) is a famous Information Retrieval (IR) technique that tries to overcome the problems of lexical matching using conceptual indexing. LSI is a variant of vector space model and proved to be 30% more effective. Many studies have reported that good retrieval performance is related to the use of various retrieval heuristics. In this paper, we focus on optimising two LSI retrieval heuristics: term weighting and rank approximation. The results obtained demonstrate that the LSI performance improves significantly with the combination of optimised term weighting and rank approximation.
引用
收藏
页码：97 / 105
页数：9
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