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In this paper, we propose a Bayesian learning approach to promoting diversity for information retrieval in biomedicine and a re-ranking model to improve retrieval performance in the biomedical domain. First, the re-ranking model computes the maximum posterior probability of the hidden property corresponding to each retrieved passage. Then it iteratively groups the passages into subsets according to their properties. Finally, these passages are re-ranked from the subsets as our output. There isdoi:10.1145/1571941.1571995 dblp:conf/sigir/HuangH09 fatcat:hviqlvzdxnct7ialzoug26yjnq