Optimising the Heuristics in Latent Semantic Indexing for Effective Information Retrieval

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.