A MARKOV RANDOM FIELD TOPIC SPACE MODEL FOR DOCUMENT RETRIEVAL

This paper proposes a novel statistical approach to intelligent document re-trieval. 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 docu-ments 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 decom-position in a matter similar to LSA to reduce dimensionality of document-term relationships to that of a latent topic space. Experimental results con-firm the ability of this approach to effectively and efficiently retrieve docu