An improved markov random field model for supporting verbose queries

Recent work in supervised learning of term-based retrieval models has shown significantly improved accuracy can often be achieved via better model estimation [2, 10, 11, 17]. In this paper, we show retrieval accuracy with Metzler and Croft’s Markov random field (MRF) approach [20] can be similarly improved via supervised learning. While the original MRF method estimates a parameter for each of its three feature classes from data, parameters within each class are set via a uniform weighting scheme adopted from the standard unigram. We conjecture greater MRF retrieval accuracy should be possible by better estimating within-class parameters, particularly for verbose queries employing natural language terms. Retrieval experiments with these queries on three TREC document collections show our improved MRF consistently out-performs both the original MRF and supervised unigram baselines. Additional experiments using blind-feedback [15] and evaluation with optimal weighting demonstrate bo...

• Type: Conference
• Year: 2009
• Where: SIGIR
• Authors: Matthew Lease
• Keywords: Information Retrieval, MRF Retrieval Accuracy, Original Mrf, Retrieval Accuracy, SIGIR 2009