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UMass Amherst and UT Austin @ the TREC 2009 Relevance Feedback Track
2009
Text Retrieval Conference
We present a new supervised method for estimating term-based retrieval models and apply it to weight expansion terms from relevance feedback. While previous work on supervised feedback [Cao et al., 2008] demonstrated significantly improved retrieval accuracy over standard unsupervised approaches [Lavrenko and Croft, 2001, Zhai and Lafferty, 2001] , feedback terms were assumed to be independent in order to reduce training time. In contrast, we adapt the AdaRank learning algorithm [Xu and Li,
dblp:conf/trec/CartrightSL09
fatcat:wgbxmmwqlffj3opjp4i65ejora