Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A dual index model for contextual information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Concept-based biomedical text retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A platform for Okapi-based contextual information retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A bayesian learning approach to promoting diversity in ranking for biomedical information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Passage extraction and result combination for genomics information retrieval
Journal of Intelligent Information Systems
A dynamic window based passage extraction algorithm for genomics information retrieval
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
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The use of large-scale experimental techniques and biomedical tools has increased the pace at which biologists produce useful information. This promotes us to propose a Bayesian model for learning and re-ranking to boost genomics information retrieval performance. We first describe a general model for discovering the property of each passage. Then, we examine a Bernoulli distribution as the prior distribution and provide an efficient way to obtain the training passages for parameter estimation, according to the characterizations of the Bernoulli distribution. Later, we evaluate our proposed model by conducting extensive experiments on the TREC 2007 and 2006 Genomics data sets. The experimental results show the effectiveness of the proposed model for improving performance on two years' TREC Genomics data sets. Furthermore, the conclusions and future prospects are also discussed.