Incorporating relevance and psuedo-relevance feedback in the markov random field model: Brown at the TREC'08 relevance feedback track

We present a new document retrieval approach combining relevance feedback, pseudo-relevance feedback, and Markov random field modeling of term interaction. Overall effectiveness of our combined model and the relative contribution from each component is evaluated on the GOV2 webpage collection. Given 0-5 feedback documents, we find each component contributes unique value to the overall ensemble, achieving significant improvement individually and in combination. Comparative evaluation in the 2008 TREC Relevance Feedback track further shows our complete system typically performs as well or better than peer systems.

[1]  W. Bruce Croft,et al.  Latent concept expansion using markov random fields , 2007, SIGIR.

[2]  W. Bruce Croft,et al.  A language modeling approach to information retrieval , 1998, SIGIR '98.

[3]  James Allan,et al.  Regression Rank: Learning to Meet the Opportunity of Descriptive Queries , 2009, ECIR.

[4]  M. de Rijke,et al.  A few examples go a long way: constructing query models from elaborate query formulations , 2008, SIGIR '08.

[5]  James Allan,et al.  Evaluating topic models for information retrieval , 2008, CIKM '08.

[6]  James Allan,et al.  Minimal test collections for retrieval evaluation , 2006, SIGIR.

[7]  W. Bruce Croft,et al.  Indri: A language-model based search engine for complex queries1 , 2005 .

[8]  W. Bruce Croft,et al.  Relevance-Based Language Models , 2001, SIGIR '01.

[9]  John D. Lafferty,et al.  Model-based feedback in the language modeling approach to information retrieval , 2001, CIKM '01.

[10]  W. Bruce Croft,et al.  Linear feature-based models for information retrieval , 2007, Information Retrieval.

[11]  James Allan,et al.  A comparison of statistical significance tests for information retrieval evaluation , 2007, CIKM '07.

[12]  Matthew Lease,et al.  A Dirichlet-Smoothed Bigram Model for Retrieving Spontaneous Speech , 2007, CLEF.

[13]  James Allan,et al.  INQUERY and TREC-8 , 1998, TREC.

[14]  Chris Buckley,et al.  Relevance Feedback Track Overview: TREC 2008 , 2008, TREC.

[15]  W. Bruce Croft,et al.  Indri at TREC 2005: Terabyte Track , 2005, TREC.

[16]  CHENGXIANG ZHAI,et al.  A study of smoothing methods for language models applied to information retrieval , 2004, TOIS.

[17]  J. J. Rocchio,et al.  Relevance feedback in information retrieval , 1971 .

[18]  Djoerd Hiemstra,et al.  Bayesian extension to the language model for ad hoc information retrieval , 2003, SIGIR.

[19]  W. Bruce Croft,et al.  Discovering key concepts in verbose queries , 2008, SIGIR '08.

[20]  Emine Yilmaz,et al.  A statistical method for system evaluation using incomplete judgments , 2006, SIGIR.

[21]  W. Bruce Croft,et al.  A Markov random field model for term dependencies , 2005, SIGIR '05.