  This paper proposes a novel statistical approach to intelligent document
retrieval. It seeks to offer a more structured and extensible mathematical
approach to the term generalization done in the popular Latent Semantic
Analysis (LSA) approach to document indexing. A Markov Random Field (MRF) is
presented that captures relationships between terms and documents as
probabilistic dependence assumptions between random variables. From there, it
uses the MRF-Gibbs equivalence to derive joint probabilities as well as local
probabilities for document variables. A parameter learning method is proposed
that utilizes rank reduction with singular value decomposition in a matter
similar to LSA to reduce dimensionality of document-term relationships to that
of a latent topic space. Experimental results confirm the ability of this
approach to effectively and efficiently retrieve documents from substantial
data sets.
