Publication | Closed Access
A Probabilistic Fusion Framework
19
Citations
37
References
2016
Year
Unknown Venue
EngineeringMachine LearningIntelligent Information RetrievalMulti-sensor Information FusionDocument ListsCorpus LinguisticsFusion TaskText MiningNatural Language ProcessingInformation RetrievalData ScienceData MiningUncertainty QuantificationPattern RecognitionComputational LinguisticsDocument ClassificationSensor FusionDecision FusionData FusionKnowledge DiscoveryTerminology ExtractionProbability TheoryComputer ScienceInformation ExtractionProbabilistic FrameworkKeyword ExtractionProbabilistic Fusion Framework
There are numerous methods for fusing document lists retrieved from the same corpus in response to a query. Many of these methods are based on seemingly unrelated techniques and heuristics. Herein we present a probabilistic framework for the fusion task. The framework provides a formal basis for deriving and explaining many fusion approaches and the connections between them. Instantiating the framework using various estimates yields novel fusion methods, some of which significantly outperform state-of-the-art approaches.
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