Search (400 results, page 2 of 20)

  • × theme_ss:"Retrievalstudien"
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  1. Harman, D.K.: Overview of the fourth Text Retrieval Conference : TREC-4 (1996) 0.03 
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    Gaithersburgh, MD : National Institute of Standards and Technology
  2. Sanderson, M.; Ruthven, I.: Report on the Glasgow IR group (glair4) submission (1997) 0.03 
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    Gaithersburgh, MD : National Institute of Standards and Technology
  3. Kelledy, L.; Smeaton, A.F.: TREC-5 experiments at Dublin City University : Query space reduction, Spanish & character shape encoding (1997) 0.03 
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    Gaithersburgh, MD : National Institute of Standards and Technology
  4. Singhal, A.; Buckley, C.; Mitra, M.: Using query zoning and correlation with SMART : TREC 5 (1997) 0.03 
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    Gaithersburgh, MD : National Institute of Standards and Technology
  5. Burnett, M.; Jones, R.; Pape, L.: InTEXT automatic query enhancements in TREC-5 (1997) 0.03 
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    Gaithersburgh, MD : National Institute of Standards and Technology
  6. Lai, K.F.; Ngo, C.-W.: Experiments on routing, filtering and Chinese text retrieval in TREC-5 (1997) 0.03 
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    Gaithersburgh, MD : National Institute of Standards and Technology
  7. Ravin, Y.: ¬The GURU system in TREC-5 (1997) 0.03 
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    Gaithersburgh, MD : National Institute of Standards and Technology
  8. Lewis, D.D.: ¬The TREC-5 filtering track (1997) 0.03 
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    Gaithersburgh, MD : National Institute of Standards and Technology
  9. Beaulieu, M.M.; Gatford, M.; Huang, X.; Robertson, S.E.; Walker, S.; Williams, P.: Okapi an TREC-5 (1997) 0.03 
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    Gaithersburgh, MD : National Institute of Standards and Technology
  10. Strzalkowski, T.; Sparck Jones, K.: NLP track at TREC-5 (1997) 0.03 
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    Gaithersburgh, MD : National Institute of Standards and Technology
  11. Clarke, C.L.A.; Cormack, G.V.: Interactive substring retrieval : multitext experiments for TREC-5 (1997) 0.03 
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    Gaithersburgh, MD : National Institute of Standards and Technology
  12. Hawking, D.; Thistlewaite, P.; Bailey, P.: ANU/ACSys TREC-5 experiments : TREC-5 report (1997) 0.03 
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  13. Kwok, K.L.; Grunfeld, L.: TREC-5 English and Chinese retrieval experiments using PIRCS (1997) 0.03 
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    Gaithersburgh, MD : National Institute of Standards and Technology
  14. Cowie, J.; Guan, Z.: CRL English routing system for TREC-5 (1997) 0.03 
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    Gaithersburgh, MD : National Institute of Standards and Technology
  15. Savoy, J.; Calvé, A. le; Vrajitoru, D.: Report on the TREC5 experiment : data fusion and collection fusion (1997) 0.03 
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    Gaithersburgh, MD : National Institute of Standards and Technology
  16. Voorhees, E.M.; Harman, D.K.: Overview of the fifth Text Retrieval Conference (TREC-5) (1997) 0.03 
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  17. Davis, M.: New experiments in cross-language text retrieval at NMSU's computing research lab (1997) 0.03 
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  18. Kekäläinen, J.; Järvelin, K.: Using graded relevance assessments in IR evaluation (2002) 0.03 
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    Abstract
    Kekalainen and Jarvelin use what they term generalized, nonbinary recall and precision measures where recall is the sum of the relevance scores of the retrieved documents divided by the sum of relevance scores of all documents in the data base, and precision is the sum of the relevance scores of the retrieved documents divided by the number of documents where the relevance scores are real numbers between zero and one. Using the In-Query system and a text data base of 53,893 newspaper articles with 30 queries selected from those for which four relevance categories to provide recall measures were available, search results were evaluated by four judges. Searches were done by average key term weight, Boolean expression, and by average term weight where the terms are grouped by a synonym operator, and for each case with and without expansion of the original terms. Use of higher standards of relevance appears to increase the superiority of the best method. Some methods do a better job of getting the highly relevant documents but do not increase retrieval of marginal ones. There is evidence that generalized precision provides more equitable results, while binary precision provides undeserved merit to some methods. Generally graded relevance measures seem to provide additional insight into IR evaluation.
    Source
    Journal of the American Society for Information Science and technology. 53(2002) no.13, S.1120-xxxx
  19. Toepfer, M.; Seifert, C.: Content-based quality estimation for automatic subject indexing of short texts under precision and recall constraints 0.03 
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    Abstract
    Semantic annotations have to satisfy quality constraints to be useful for digital libraries, which is particularly challenging on large and diverse datasets. Confidence scores of multi-label classification methods typically refer only to the relevance of particular subjects, disregarding indicators of insufficient content representation at the document-level. Therefore, we propose a novel approach that detects documents rather than concepts where quality criteria are met. Our approach uses a deep, multi-layered regression architecture, which comprises a variety of content-based indicators. We evaluated multiple configurations using text collections from law and economics, where the available content is restricted to very short texts. Notably, we demonstrate that the proposed quality estimation technique can determine subsets of the previously unseen data where considerable gains in document-level recall can be achieved, while upholding precision at the same time. Hence, the approach effectively performs a filtering that ensures high data quality standards in operative information retrieval systems.
  20. Saracevic, T.: On a method for studying the structure and nature of requests in information retrieval (1983) 0.03 
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    Pages
    S.22-25
    Series
    Proceedings of the American Society for Information Science; vol. 20
    Source
    Productivity in the information age : proceedings of the 46th ASIS annual meeting, 1983. Ed.: Raymond F Vondra

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