In this paper we present a theoretical model for understanding the performance of Latent
Semantic Indexing (LSI) search and retrieval application. Many models for understanding
LSI have been proposed. Ours is the first to study the values produced by LSI in the term by
dimension vectors. The framework presented here is based on term co-occurrence data. We
show a strong correlation between second-order term co-occurrence and the values
produced by the Singular Value Decomposition (SVD) algorithm that forms the foundation …