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2009
ACM

An improved markov random field model for supporting verbose queries

13 years 15 days ago
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...
Matthew Lease
Added 28 May 2010
Updated 28 May 2010
Type Conference
Year 2009
Where SIGIR
Authors Matthew Lease
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