On the performance of Latent Semantic Indexing-based Information Retrieval

Abstract

Conventional vector-based Information Retrieval (IR) models: Vector Space Model (VSM) and Generalized Vector Space Model (GVSM) represents documents and queries as vectors in a multidimensional space. This high dimensional data places great demands on computing resources. To overcome these problems, Latent Semantic Indexing (LSI), a variant of VSM, projects the documents into a lower dimensional space. It is stated in IR literature that LSI model is 30% more effective than classical VSM models. However, statistical significance tests are required to evaluate the reliability of such comparisons. Focus of this paper is to address this issue. We discuss the tradeoffs of VSM, GVSM, LSI and evaluate the difference in performance on four testing document collections. Then we analyze the statistical significance of these performance differences.