CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Boosting novelty for biomedical information retrieval through probabilistic latent semantic analysis
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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In this paper, we propose a reranking model to improve the aspect-level performance in the biomedical domain. This model iteratively computes the maximum hidden aspect for every retrieved passage and then reranks these passages from aspect subsets. The experimental results show the improvements of the aspect-level performance up to 27.14% for 2006 Genomics topics and 27.09% for 2007 Genomics topics.