Search (94 results, page 1 of 5)

  • × theme_ss:"Retrievalstudien"
  1. 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
  2. Ellis, D.: Progress and problems in information retrieval (1996) 0.05 
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    Date
    26. 7.2002 20:22:46
    Footnote
    Rez. in: Managing information 3(1996) no.10, S.49 (D. Bawden); Program 32(1998) no.2, S.190-192 (C. Revie)
  3. Oppenheim, C.; Morris, A.; McKnight, C.: ¬The evaluation of WWW search engines (2000) 0.04 
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    Abstract
    The literature of the evaluation of Internet search engines is reviewed. Although there have been many studies, there has been little consistency in the way such studies have been carried out. This problem is exacerbated by the fact that recall is virtually impossible to calculate in the fast changing Internet environment, and therefore the traditional Cranfield type of evaluation is not usually possible. A variety of alternative evaluation methods has been suggested to overcome this difficulty. The authors recommend that a standardised set of tools is developed for the evaluation of web search engines so that, in future, comparisons can be made between search engines more effectively, and that variations in performance of any given search engine over time can be tracked. The paper itself does not provide such a standard set of tools, but it investigates the issues and makes preliminary recommendations of the types of tools needed
  4. Pal, S.; Mitra, M.; Kamps, J.: Evaluation effort, reliability and reusability in XML retrieval (2011) 0.03 
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    Abstract
    The Initiative for the Evaluation of XML retrieval (INEX) provides a TREC-like platform for evaluating content-oriented XML retrieval systems. Since 2007, INEX has been using a set of precision-recall based metrics for its ad hoc tasks. The authors investigate the reliability and robustness of these focused retrieval measures, and of the INEX pooling method. They explore four specific questions: How reliable are the metrics when assessments are incomplete, or when query sets are small? What is the minimum pool/query-set size that can be used to reliably evaluate systems? Can the INEX collections be used to fairly evaluate "new" systems that did not participate in the pooling process? And, for a fixed amount of assessment effort, would this effort be better spent in thoroughly judging a few queries, or in judging many queries relatively superficially? The authors' findings validate properties of precision-recall-based metrics observed in document retrieval settings. Early precision measures are found to be more error-prone and less stable under incomplete judgments and small topic-set sizes. They also find that system rankings remain largely unaffected even when assessment effort is substantially (but systematically) reduced, and confirm that the INEX collections remain usable when evaluating nonparticipating systems. Finally, they observe that for a fixed amount of effort, judging shallow pools for many queries is better than judging deep pools for a smaller set of queries. However, when judging only a random sample of a pool, it is better to completely judge fewer topics than to partially judge many topics. This result confirms the effectiveness of pooling methods.
    Date
    22. 1.2011 14:20:56
  5. Prabha, C.: ¬The large retrieval phenomenon (1991) 0.02 
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  6. Toepfer, M.; Seifert, C.: Content-based quality estimation for automatic subject indexing of short texts under precision and recall constraints 0.02 
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    Content
    This is an authors' manuscript version of a paper accepted for proceedings of TPDL-2018, Porto, Portugal, Sept 10-13. The nal authenticated publication is available online at https://doi.org/will be added as soon as available.
  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. Buckley, C.; Voorhees, E.M.: Retrieval system evaluation (2005) 0.02 
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  12. Buckley, C.; Singhal, A.; Mitra, M.; Salton, G.: New retrieval approaches using SMART : TREC 4 (1996) 0.02 
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  13. Burnett, M.; Fisher, C.; Jones, R.: InTEXT precision indexing in TREC4 (1996) 0.02 
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  14. Singhal, A.; Buckley, C.; Mitra, M.: Using query zoning and correlation with SMART : TREC 5 (1997) 0.02 
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  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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  16. Pedersen, J.O.; Silverstein, C.; Vogt, C.C.: Verity at TREC-6 : out-of-the-box and beyond (2000) 0.02 
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  17. Cole, C.: Intelligent information retrieval : Part IV: Testing the timing of two information retrieval devices in a naturalistic setting (2001) 0.02 
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  18. Buckley, C.; Voorhees, E.M.: Retrieval evaluation with incomplete information (2004) 0.02 
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  19. Buckley, C.: ¬The SMART Project at TREC (2005) 0.02 
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  20. Petras, V.; Womser-Hacker, C.: Evaluation im Information Retrieval (2023) 0.02 
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    Abstract
    Das Ziel einer Evaluation ist die Überprüfung, ob bzw. in welchem Ausmaß ein Informationssystem die an das System gestellten Anforderungen erfüllt. Informationssysteme können aus verschiedenen Perspektiven evaluiert werden. Für eine ganzheitliche Evaluation (als Synonym wird auch Evaluierung benutzt), die unterschiedliche Qualitätsaspekte betrachtet (z. B. wie gut ein System relevante Dokumente rankt, wie schnell ein System die Suche durchführt, wie die Ergebnispräsentation gestaltet ist oder wie Suchende durch das System geführt werden) und die Erfüllung mehrerer Anforderungen überprüft, empfiehlt es sich, sowohl eine perspektivische als auch methodische Triangulation (d. h. der Einsatz von mehreren Ansätzen zur Qualitätsüberprüfung) vorzunehmen. Im Information Retrieval (IR) konzentriert sich die Evaluation auf die Qualitätseinschätzung der Suchfunktion eines Information-Retrieval-Systems (IRS), wobei oft zwischen systemzentrierter und nutzerzentrierter Evaluation unterschieden wird. Dieses Kapitel setzt den Fokus auf die systemzentrierte Evaluation, während andere Kapitel dieses Handbuchs andere Evaluationsansätze diskutieren (s. Kapitel C 4 Interaktives Information Retrieval, C 7 Cross-Language Information Retrieval und D 1 Information Behavior).

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  • e 76
  • d 14
  • chi 1
  • f 1
  • m 1
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  • a 87
  • s 6
  • m 5
  • el 1
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