Search (78 results, page 1 of 4)

  • × theme_ss:"Retrievalalgorithmen"
  1. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.05 
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    Date
    20. 1.2007 18:30:22
    Source
    Aslib proceedings. 56(2004) no.4, S.201-211
  2. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.05 
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    Date
    22. 3.2003 19:27:23
    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.4, S.285-301
  3. Crestani, F.; Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Mathematical, logical, and formal methods in information retrieval : an introduction to the special issue (2003) 0.03 
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    Date
    22. 3.2003 19:27:36
    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.4, S.281-284
  4. Campos, L.M. de; Fernández-Luna, J.M.; Huete, J.F.: Implementing relevance feedback in the Bayesian network retrieval model (2003) 0.03 
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    Date
    22. 3.2003 19:30:19
    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.4, S.302-313
  5. Burgin, R.: ¬The retrieval effectiveness of 5 clustering algorithms as a function of indexing exhaustivity (1995) 0.03 
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    Abstract
    The retrieval effectiveness of 5 hierarchical clustering methods (single link, complete link, group average, Ward's method, and weighted average) is examined as a function of indexing exhaustivity with 4 test collections (CR, Cranfield, Medlars, and Time). Evaluations of retrieval effectiveness, based on 3 measures of optimal retrieval performance, confirm earlier findings that the performance of a retrieval system based on single link clustering varies as a function of indexing exhaustivity but fail ti find similar patterns for other clustering methods. The data also confirm earlier findings regarding the poor performance of single link clustering is a retrieval environment. The poor performance of single link clustering appears to derive from that method's tendency to produce a small number of large, ill defined document clusters. By contrast, the data examined here found the retrieval performance of the other clustering methods to be general comparable. The data presented also provides an opportunity to examine the theoretical limits of cluster based retrieval and to compare these theoretical limits to the effectiveness of operational implementations. Performance standards of the 4 document collections examined were found to vary widely, and the effectiveness of operational implementations were found to be in the range defined as unacceptable. Further improvements in search strategies and document representations warrant investigations
    Date
    22. 2.1996 11:20:06
  6. Efthimiadis, E.N.: User choices : a new yardstick for the evaluation of ranking algorithms for interactive query expansion (1995) 0.03 
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    Date
    22. 2.1996 13:14:10
    Source
    Information processing and management. 31(1995) no.4, S.605-620
  7. Song, D.; Bruza, P.D.: Towards context sensitive information inference (2003) 0.03 
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    Date
    22. 3.2003 19:35:46
    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.4, S.321-334
  8. Shiri, A.A.; Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment : a user-centered evaluation (2006) 0.03 
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    Date
    22. 7.2006 16:32:43
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.4, S.462-478
  9. Dominich, S.: Mathematical foundations of information retrieval (2001) 0.03 
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    Date
    22. 3.2008 12:26:32
    Isbn
    0-7923-6861-4
  10. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.03 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  11. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.02 
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    Date
    30. 3.2001 13:32:22
  12. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.02 
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    Date
    25. 8.2005 17:42:22
  13. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.02 
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    14. 6.2015 22:12:44
  14. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.02 
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    14. 6.2015 22:12:56
  15. Clarke, C.L.A.; Cormack, G.V.; Burkowski, F.J.: Shortest substring ranking : multitext experiments for TREC-4 (1996) 0.02 
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    Source
    The Fourth Text Retrieval Conference (TREC-4). Ed.: K. Harman
  16. Savoy, J.; Ndarugendamwo, M.; Vrajitoru, D.: Report on the TREC-4 experiment : combining probabilistic and vector-space schemes (1996) 0.02 
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    The Fourth Text Retrieval Conference (TREC-4). Ed.: K. Harman
  17. Daniowicz, C.; Baliski, J.: Document ranking based upon Markov chains (2001) 0.01 
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    Source
    Information processing and management. 37(2001) no.4, S.623-637
  18. Faloutsos, C.: Signature files (1992) 0.01 
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    Date
    7. 5.1999 15:22:48
  19. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.01 
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