A dual index model for contextual information retrieval
Proceedings of the 28th 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
Genomics information retrieval using a Bayesian model for learning and re-ranking
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Expert Systems with Applications: An International Journal
Using semantic-based association rule mining for improving clinical text retrieval
HIS'13 Proceedings of the second international conference on Health Information Science
Exploiting semantics for improving clinical information retrieval
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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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We present an extensible java-based platform for contextual retrieval based on the probabilistic information retrieval model. Modules for dual indexes, relevance feedback with blind or machine learning approaches and query expansion with context are integrated into the Okapi system to deal with the contextual information. This platform allows easy extension to include other types of contextual information.