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Deliberation scheduling for problem solving in time-constrained environments
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Optimal composition of real-time systems
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On relevance weights with little relevance information
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Solving the word mismatch problem through automatic text analysis
Translingual information retrieval: learning from bilingual corpora
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Local Feedback in Full-Text Retrieval Systems
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This paper presents a new approach to information retrieval (IR) based on run-time selection of the best set of techniques to respond to a given query. A technique is selected based on its projected effectiveness with respect to the specific query, the load on the system, and a time-dependent utility function. The paper examines two fundamental questions: (1) can the selection of the best IR techniques be performed at run-time with minimal computational overhead? and (2) is it possible to construct a reliable probabilistic model of the performance of an IR technique that is conditioned on the characteristics of the query? We show that both of these questions can be answered positively. These results suggest a new system design that carries a great potential to improve the quality of service of future IR systems.