Search (37 results, page 1 of 2)

  • × theme_ss:"Retrievalstudien"
  • × year_i:[2000 TO 2010}
  1. Larsen, B.; Ingwersen, P.; Lund, B.: Data fusion according to the principle of polyrepresentation (2009) 0.00
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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
  2. Serrano Cobos, J.; Quintero Orta, A.: Design, development and management of an information recovery system for an Internet Website : from documentary theory to practice (2003) 0.00
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    Source
    Challenges in knowledge representation and organization for the 21st century: Integration of knowledge across boundaries. Proceedings of the 7th ISKO International Conference Granada, Spain, July 10-13, 2002. Ed.: M. López-Huertas
  3. Harman, D.K.: ¬The TREC test collections (2005) 0.00
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    Date
    29. 3.1996 18:16:49
  4. Buckley, C.; Voorhees, E.M.: Retrieval system evaluation (2005) 0.00
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    Date
    29. 3.1996 18:16:49
  5. Voiskunskii, V.G.: Evaluation of search results (2000) 0.00
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    Source
    Encyclopedia of library and information science. Vol.66, [=Suppl.29]
  6. Harman, D.K.: ¬The TREC ad hoc experiments (2005) 0.00
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    Date
    29. 3.1996 18:16:49
  7. Robertson, S.; Callan, J.: Routing and filtering (2005) 0.00
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    Date
    29. 3.1996 18:16:49
  8. Beaulieu, M.: Approaches to user-based studies in information seeking and retrieval : a Sheffield perspective (2003) 0.00
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    Source
    Journal of information science. 29(2003) no.4, S.239-248
  9. Harman, D.K.: Beyond English (2005) 0.00
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    Date
    29. 3.1996 18:16:49
  10. Voorhees, E.M.; Garofolo, J.S.: Retrieving noisy text (2005) 0.00
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    Date
    29. 3.1996 18:16:49
  11. Voorhees, E.M.; Harman, D.: Overview of the Sixth Text REtrieval Conference (TREC-6) (2000) 0.00
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    Date
    11. 8.2001 16:22:19
  12. Voorhees, E.M.: Question answering in TREC (2005) 0.00
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    Date
    29. 3.1996 18:16:49
  13. Cormack, G.V.; Clarke, C.L.A.; Palmer, C.R.; Lynam, T.R.: MultiText experiments for TREC (2005) 0.00
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    Date
    29. 3.1996 18:16:49
  14. Dumais, S.T.; Belkin, N.J.: ¬The TREC interactive tracks : putting the user into search (2005) 0.00
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    Date
    29. 3.1996 18:16:49
  15. Hawking, D.; Craswell, N.: ¬The very large collection and Web tracks (2005) 0.00
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    Date
    29. 3.1996 18:16:49
  16. Allan, J.; Croft, W.B.; Callan, J.: ¬The University of Massachusetts and a dozen TRECs (2005) 0.00
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    Date
    29. 3.1996 18:16:49
  17. Robertson, S.: How Okapi came to TREC (2005) 0.00
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    Date
    29. 3.1996 18:16:49
  18. Buckley, C.: ¬The SMART Project at TREC (2005) 0.00
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    Date
    29. 3.1996 18:16:49
  19. Kwok, K.-L.: Ten years of ad hoc retrieval at TREC using PIRCS (2005) 0.00
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    Date
    29. 3.1996 18:16:49
  20. Hiemstra, D.; Kraaij, W.: ¬A language-modeling approach to TREC (2005) 0.00
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    Date
    29. 3.1996 18:16:49

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