Search (157 results, page 1 of 8)

  • × theme_ss:"Retrievalalgorithmen"
  • × year_i:[2000 TO 2010}
  1. Crestani, F.: Combination of similarity measures for effective spoken document retrieval (2003) 0.02 
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    Source
    Journal of information science. 29(2003) no.2, S.87-96
  2. 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
    Source
    Library and information research news. 24(2000) no.77, S.30-34
  3. Archuby, C.G.: Interfaces se recuperacion para catalogos en linea con salidas ordenadas por probable relevancia (2000) 0.02 
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    Date
    29. 1.1996 18:23:13
    Source
    Ciencia da informacao. 29(2000) no.3, S.5-13
  4. Okada, M.; Ando, K.; Lee, S.S.; Hayashi, Y.; Aoe, J.I.: ¬An efficient substring search method by using delayed keyword extraction (2001) 0.02 
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    Date
    29. 3.2002 17:24:03
    Source
    Information processing and management. 37(2001) no.5, S.741-761
  5. Thompson, P.: Looking back: on relevance, probabilistic indexing and information retrieval (2008) 0.01 
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    Date
    31. 7.2008 19:58:29
    Source
    Information processing and management. 44(2008) no.2, S.963-970
  6. Vechtomova, O.; Karamuftuoglu, M.: Lexical cohesion and term proximity in document ranking (2008) 0.01 
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    Date
    1. 8.2008 12:29:05
    Source
    Information processing and management. 44(2008) no.4, S.1485-1502
  7. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.01 
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    Date
    20. 1.2007 18:30:22
    Source
    Aslib proceedings. 56(2004) no.4, S.201-211
  8. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.01 
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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
  9. Otterbacher, J.; Erkan, G.; Radev, D.R.: Biased LexRank : passage retrieval using random walks with question-based priors (2009) 0.01 
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    Date
    22.11.2008 17:11:29
    Source
    Information processing and management. 45(2009) no.1, S.42-54
  10. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.01 
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    Date
    20. 3.2005 16:23:22
    Source
    Information - Wissenschaft und Praxis. 56(2005) H.2, S.87-92
  11. 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) 0.01 
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    Date
    22. 3.2003 19:27:36
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    Journal of the American Society for Information Science and technology. 54(2003) no.4, S.281-284
  12. Cannane, A.; Williams, H.E.: General-purpose compression for efficient retrieval (2001) 0.01 
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    Date
    29. 9.2001 13:59:55
    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.5, S.430-437
  13. Kaszkiel, M.; Zobel, J.: Effective ranking with arbitrary passages (2001) 0.01 
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    Date
    29. 9.2001 14:00:39
    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.4, S.344-364
  14. Bodoff, D.; Enache, D.; Kambil, A.; Simon, G.; Yukhimets, A.: ¬A unified maximum likelihood approach to document retrieval (2001) 0.01 
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    Date
    29. 9.2001 17:52:51
    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.10, S.785-796
  15. Drucker, H.; Shahrary, B.; Gibbon, D.C.: Support vector machines : relevance feedback and information retrieval (2002) 0.01 
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    Date
    15. 8.2004 18:55:29
    Source
    Information processing and management. 38(2002) no.3, S.305-323
  16. Käki, M.: fKWIC: frequency-based Keyword-in-Context Index for filtering Web search results (2006) 0.01 
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    Abstract
    Enormous Web search engine databases combined with short search queries result in large result sets that are often difficult to access. Result ranking works fairly well, but users need help when it fails. For these situations, we propose a filtering interface that is inspired by keyword-in-context (KWIC) indices. The user interface lists the most frequent keyword contexts (fKWIC). When a context is selected, the corresponding results are displayed in the result list, allowing users to concentrate on the specific context. We compared the keyword context index user interface to the rank order result listing in an experiment with 36 participants. The results show that the proposed user interface was 29% faster in finding relevant results, and the precision of the selected results was 19% higher. In addition, participants showed positive attitudes toward the system.
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.12, S.1606-1615
  17. Kekäläinen, J.: Binary and graded relevance in IR evaluations : comparison of the effects on ranking of IR systems (2005) 0.01 
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    Date
    26.12.2007 20:29:18
    Source
    Information processing and management. 41(2005) no.5, S.1019-1034
  18. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.01 
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    Date
    31. 5.2004 19:22:06
    Source
    Journal of the American Society for Information Science and technology. 55(2004) no.7, S.628-636
  19. Furner, J.: ¬A unifying model of document relatedness for hybrid search engines (2003) 0.01 
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    Date
    11. 9.2004 17:32:22
    Pages
    S.245-250
  20. Witschel, H.F.: Global term weights in distributed environments (2008) 0.01 
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    Date
    1. 8.2008 9:44:22
    Source
    Information processing and management. 44(2008) no.3, S.1049-1061

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