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Relevance feedback, which uses the terms in relevant documents to enrich the user’s initial query, is an effective method for improving retrieval performance. An associated key research problem is the following: Which documents to present to the user so that the user’s feedback on the documents can significantly impact relevance feedback performance. This(More)
The classical probabilistic models attempt to capture the Ad hoc information retrieval problem within a rigorous probabilistic framework. It has long been recognized that the primary obstacle to effective performance of the probabilistic models is the need to estimate a relevance model. The Dirichlet compound multinomial (DCM) distribution, which relies on(More)
Relevance feedback has been demonstrated to be an effective strategy for improving retrieval accuracy. The existing relevance feedback algorithms based on language models and vector space models are not effective in learning from negative feedback documents, which are abundant if the initial query is difficult. The probabilistic retrieval model has the(More)
Active learning algorithms actively select training examples to acquire labels from domain experts, which are very effective to reduce human labeling effort in the context of supervised learning. To reduce computational time in training, as well as provide more convenient user interaction environment, it is necessary to select batches of new training(More)
We provide an automation perspective on modeling knowledge services. We consider a service center as the atomic unit for building networks of enterprises, suppliers, and customers. We provide an approach to integrate knowledge and resource management in service centers. We describe specific models and solutions for optimized information and knowledge(More)
Text clustering is most commonly treated as a fully automated task without user supervision. However, we can improve clustering performance using supervision in the form of pairwise (must-link and cannot-link) constraints. This paper introduces a rigorous Bayesian framework for semi-supervised clustering which incorporates human supervision in the form of(More)
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