A Markov Random Field Topic Space Model for Document Retrieval
Abstract
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.
Cite
@article{arxiv.1111.6640,
title = {A Markov Random Field Topic Space Model for Document Retrieval},
author = {Scott Hand},
journal= {arXiv preprint arXiv:1111.6640},
year = {2011}
}