Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
379095 | Data & Knowledge Engineering | 2008 | 18 Pages |
Abstract
This paper presents an analysis of several different LSI (latent semantic indexing) query approaches and proposes a novel rescaling technique, namely singular value rescaling (SVR). Experiments on a standardized TREC data set confirmed the effectiveness of SVR, showing an improvement ratio of 5.9% over the best conventional LSI query approach. In addition, we also compared SVR with another scaling technique in text retrieval called iterative residual rescaling (IRR). Experiments on TREC data set show that SVR performs better than IRR.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Hua Yan, William I. Grosky, Farshad Fotouhi,