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Bodoff, D.; Wu, B.; Wong, K.Y.M.: Relevance data for language models using maximum likelihood (2003)
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- Abstract
- We present a preliminary empirical test of a maximum likelihood approach to using relevance data for training information retrieval (IR) parameters. Similar to language models, our method uses explicitly hypothesized distributions for documents and queries, but we add to this an explicitly hypothesized distribution for relevance judgments. The method unifies document-oriented and query-oriented views. Performance is better than the Rocchio heuristic for document and/or query modification. The maximum likelihood methodology also motivates a heuristic estimate of the MLE optimization. The method can be used to test competing hypotheses regarding the processes of authors' term selection, searchers' term selection, and assessors' relevancy judgments.
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Bodoff, D.; Raban, D.: Question types and intermediary elicitations (2016)
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- Date
- 22. 1.2016 11:58:25
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Bodoff, D.; Enache, D.; Kambil, A.; Simon, G.; Yukhimets, A.: ¬A unified maximum likelihood approach to document retrieval (2001)
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Bodoff, D.; Raban, D.: User models as revealed in web-based research services (2012)
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Bodoff, D.: ¬A re-unification of two competing models for document retrieval (1999)
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Bodoff, D.: Relevance for browsing, relevance for searching (2006)
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Bodoff, D.; Kambil, A.: Partial coordination : I. The best of pre-coordination and post-coordination (1998)
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Bodoff, D.; Kambil, A.: Partial coordination : II. A preliminary evaluation and failure analysis (1998)
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Bodoff, D.; Robertson, S.: ¬A new unified probabilistic model (2004)
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Bodoff, D.; Wong, S.P.-S.: Documents and queries as random variables : history and implications (2006)
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Bodoff, D.: Emergence of terminological conventions as a searcher-indexer coordination game (2009)
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Bodoff, D.; Richter-Levin, Y.: Viewpoints in indexing term assignment (2020)
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Bodoff, D.: Test theory for evaluating reliability of IR test collections (2008)
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