Search (10 results, page 1 of 1)

  • × author_ss:"Robertson, S."
  • × type_ss:"a"
  • × year_i:[2000 TO 2010}
  1. Hawking, D.; Robertson, S.: On collection size and retrieval effectiveness (2003) 0.08 
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    Date
    14. 8.2005 14:22:22
    Source
    Information retrieval. 6(2003) no.1, S.99-150
  2. Robertson, S.; Tait, J.: In Memoriam Karen Sparck Jones (2007) 0.04 
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    Date
    26.12.2007 14:22:47
    Source
    Information processing and management. 43(2007) no.6, S.1441-1446
  3. Robertson, S.; Walker, S.: Threshold setting in adaptive filtering (2000) 0.00 
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    Source
    Journal of documentation. 56(2000) no.3, S.312-331
  4. Robertson, S.; Callan, J.: Routing and filtering (2005) 0.00 
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    Pages
    S.99-122
  5. Robertson, S.: How Okapi came to TREC (2005) 0.00 
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    Pages
    S.287-300
  6. Robertson, S.; Tait, J.: Karen Sparck Jones (2008) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.5, S.852-854
  7. Willett, P.; Robertson, S.: In memoriam: Karen Sparck Jones (2007) 0.00 
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  8. Robertson, S.: On the history of evaluation in IR (2009) 0.00 
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    Pages
    S.63-93
  9. Bodoff, D.; Robertson, S.: ¬A new unified probabilistic model (2004) 0.00 
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    Abstract
    This paper proposes a new unified probabilistic model. Two previous models, Robertson et al.'s "Model 0" and "Model 3," each have strengths and weaknesses. The strength of Model 0 not found in Model 3, is that it does not require relevance data about the particular document or query, and, related to that, its probability estimates are straightforward. The strength of Model 3 not found in Model 0 is that it can utilize feedback information about the particular document and query in question. In this paper we introduce a new unified probabilistic model that combines these strengths: the expression of its probabilities is straightforward, it does not require that data must be available for the particular document or query in question, but it can utilize such specific data if it is available. The model is one way to resolve the difficulty of combining two marginal views in probabilistic retrieval.
    Source
    Journal of the American Society for Information Science and Technology. 55(2004) no.6, S.471-487
  10. Robertson, S.: Understanding inverse document frequency : on theoretical arguments for IDF (2004) 0.00 
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    Source
    Journal of documentation. 60(2004) no.5, S.503-520

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