Search (131 results, page 1 of 7)

  • × theme_ss:"Retrievalstudien"
  1. Allan, J.; Callan, J.P.; Croft, W.B.; Ballesteros, L.; Broglio, J.; Xu, J.; Shu, H.: INQUERY at TREC-5 (1997) 0.07
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    Date
    27. 2.1999 20:55:22
    Source
    The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman
  2. Ng, K.B.; Loewenstern, D.; Basu, C.; Hirsh, H.; Kantor, P.B.: Data fusion of machine-learning methods for the TREC5 routing tak (and other work) (1997) 0.06
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    Date
    27. 2.1999 20:59:22
    Source
    The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman
  3. ¬The Fifth Text Retrieval Conference (TREC-5) (1997) 0.05
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    Abstract
    Proceedings of the 5th TREC-confrerence held in Gaithersburgh, Maryland, Nov 20-22, 1996. Aim of the conference was discussion on retrieval techniques for large test collections. Different research groups used different techniques, such as automated thesauri, term weighting, natural language techniques, relevance feedback and advanced pattern matching, for information retrieval from the same large database. This procedure makes it possible to compare the results. The proceedings include papers, tables of the system results, and brief system descriptions including timing and storage information
  4. Blair, D.C.: STAIRS Redux : thoughts on the STAIRS evaluation, ten years after (1996) 0.04
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    Abstract
    The test of retrieval effectiveness performed on IBM's STAIRS and reported in 'Communications of the ACM' 10 years ago, continues to be cited frequently in the information retrieval literature. The reasons for the study's continuing pertinence to today's research are discussed, and the political, legal, and commercial aspects of the study are presented. In addition, the method of calculating recall that was used in the STAIRS study is discussed in some detail, especially how it reduces the 5 major types of uncertainty in recall estimations. It is also suggested that this method of recall estimation may serve as the basis for recall estimations that might be truly comparable between systems
    Source
    Journal of the American Society for Information Science. 47(1996) no.1, S.4-22
  5. Larsen, B.; Ingwersen, P.; Lund, B.: Data fusion according to the principle of polyrepresentation (2009) 0.03
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    Abstract
    We report data fusion experiments carried out on the four best-performing retrieval models from TREC 5. Three were conceptually/algorithmically very different from one another; one was algorithmically similar to one of the former. The objective of the test was to observe the performance of the 11 logical data fusion combinations compared to the performance of the four individual models and their intermediate fusions when following the principle of polyrepresentation. This principle is based on cognitive IR perspective (Ingwersen & Järvelin, 2005) and implies that each retrieval model is regarded as a representation of a unique interpretation of information retrieval (IR). It predicts that only fusions of very different, but equally good, IR models may outperform each constituent as well as their intermediate fusions. Two kinds of experiments were carried out. One tested restricted fusions, which entails that only the inner disjoint overlap documents between fused models are ranked. The second set of experiments was based on traditional data fusion methods. The experiments involved the 30 TREC 5 topics that contain more than 44 relevant documents. In all tests, the Borda and CombSUM scoring methods were used. Performance was measured by precision and recall, with document cutoff values (DCVs) at 100 and 15 documents, respectively. Results show that restricted fusions made of two, three, or four cognitively/algorithmically very different retrieval models perform significantly better than do the individual models at DCV100. At DCV15, however, the results of polyrepresentative fusion were less predictable. The traditional fusion method based on polyrepresentation principles demonstrates a clear picture of performance at both DCV levels and verifies the polyrepresentation predictions for data fusion in IR. Data fusion improves retrieval performance over their constituent IR models only if the models all are quite conceptually/algorithmically dissimilar and equally and well performing, in that order of importance.
    Date
    22. 3.2009 18:48:28
  6. Hawking, D.; Thistlewaite, P.; Bailey, P.: ANU/ACSys TREC-5 experiments : TREC-5 report (1997) 0.03
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    Source
    The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman
  7. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.02
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    Date
    20.10.2000 12:22:23
  8. Tomaiuolo, N.G.; Parker, J.: Maximizing relevant retrieval : keyword and natural language searching (1998) 0.02
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    Source
    Online. 22(1998) no.6, S.57-58
  9. Voorhees, E.M.; Harman, D.: Overview of the Sixth Text REtrieval Conference (TREC-6) (2000) 0.02
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    Date
    11. 8.2001 16:22:19
  10. Dalrymple, P.W.: Retrieval by reformulation in two library catalogs : toward a cognitive model of searching behavior (1990) 0.02
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    Date
    22. 7.2006 18:43:54
  11. Gauch, S.; Wang, J.: Corpus analysis for TREC 5 query expansion (1997) 0.02
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    Source
    The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman
  12. Kelledy, L.; Smeaton, A.F.: TREC-5 experiments at Dublin City University : Query space reduction, Spanish & character shape encoding (1997) 0.02
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    Source
    The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman
  13. Singhal, A.; Buckley, C.; Mitra, M.: Using query zoning and correlation with SMART : TREC 5 (1997) 0.02
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    Source
    The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman
  14. Burnett, M.; Jones, R.; Pape, L.: InTEXT automatic query enhancements in TREC-5 (1997) 0.02
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    Source
    The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman
  15. Lai, K.F.; Ngo, C.-W.: Experiments on routing, filtering and Chinese text retrieval in TREC-5 (1997) 0.02
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    Source
    The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman
  16. Ravin, Y.: ¬The GURU system in TREC-5 (1997) 0.02
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    Source
    The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman
  17. Lewis, D.D.: ¬The TREC-5 filtering track (1997) 0.02
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    Source
    The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman
  18. Beaulieu, M.M.; Gatford, M.; Huang, X.; Robertson, S.E.; Walker, S.; Williams, P.: Okapi an TREC-5 (1997) 0.02
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    Source
    The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman
  19. Strzalkowski, T.; Sparck Jones, K.: NLP track at TREC-5 (1997) 0.02
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    Source
    The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman
  20. Clarke, C.L.A.; Cormack, G.V.: Interactive substring retrieval : multitext experiments for TREC-5 (1997) 0.02
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    Source
    The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman

Years

Languages

  • e 117
  • d 10
  • f 2
  • m 1
  • More… Less…

Types

  • a 122
  • s 5
  • m 4
  • r 3
  • el 1
  • More… Less…