Text REtrieval Conference · 2001 · 42 citations · 4 references
EngineeringIntelligent Information RetrievalRelevance AssessmentsQuery ModelCorpus LinguisticsText MiningNatural Language ProcessingInformation RetrievalData ScienceComputational LinguisticsRelevance FeedbackDocument ClassificationQuery ExpansionLanguage StudiesContent AnalysisTrec-10 Web TrackTopic Relevance RetrievalKnowledge DiscoveryTerminology ExtractionTopic ModelProbabilistic FrameworkWeb TrackTopic Relevance TermLinguisticsInteractive Information Retrieval
The main investigation of our participation in the WEB track of TREC-10 concerns the e ectiveness of a novel probabilistic framework [1] for generating term weighting models of topic relevance retrieval. This approach endeavours to determine the weight of a word within a document in a purely theoretic way as a combination of di erent probability distributions, with the goal of reducing as much as possible the number of parameters which must be learned and tuned from relevance assessments on training test collections.
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