An explanation of the effectiveness of latent semantic indexing by means of a Bayesian regression model

摘要

Latent Semantic Indexing (LSI) is an effective automated method for determining if a document is relevant to a reader based on a few words or an abstract describing the reader's needs. A particular feature of LSI is its ability to deal automatically with synonyms. LSI generally is explained in terms of a mathematical concept called the Singular Value Decomposition and statistical methods such as factor analysis. This paper looks at LSI from a different perspective, comparing it to statistical regression and Bayesian methods. The relationships found can be useful in explaining the performance of LSI and in suggesting variations on the LSI approach.