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  • × theme_ss:"Retrievalstudien"
  1. ¬The Second Text Retrieval Conference : TREC-2 (1995) 0.01 
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    Content
    Enthält die Beiträge: HARMAN, D.: Overview of the Second Text Retrieval Conference (TREC-2); SPRACK JONES, K.: Reflections on TREC; BUCKLEY, C., J. ALLAN u. G. SALTON: Automatic routing and retrieval using SMART: TREC-2; CALLAN, J.P., W.B. CROFT u. J. BROGLIO: TREC and TIPSTER experiments with INQUERY; ROBERTSON, S.R., S. WALKER u. M.M. HANCOCK-BEAULIEU: Large test collection experiments on an operational, interactive system: OKAPI at TREC; ZOBEL, J., A. MOFFAT, R. WILKINSON u. R. SACKS-DAVIS: Efficient retrieval of partial documents; METTLER, M. u. F. NORDBY: TREC routing experiments with the TRW/Paracel Fast Data Finder; EVANS, D.A. u. R.G. LEFFERTS: CLARIT-TREC experiments; STRZALKOWSKI, T.: Natural language information retrieval; CAID, W.R., S.T. DUMAIS u. S.I. GALLANT: Learned vector-space models for document retrieval; BELKIN, N.J. P. KANTOR, E.A. FOX u. J.A. SHAW: Combining the evidence of multiple query representations for information retrieval
  2. Vakkari, P.; Sormunen, E.: ¬The influence of relevance levels an the effectiveness of interactive information retrieval (2004) 0.01 
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  3. Geist, K.: Qualität und Relevanz von bildungsbezogenen Suchergebnissen bei der Suche im Web (2012) 0.01 
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    Content
    Vgl. auch unter: http://www.univie.ac.at/voeb/blog/?p=23628 bzw. http://eprints.rclis.org/handle/10760/17623.
  4. Colace, F.; Santo, M. de; Greco, L.; Napoletano, P.: Improving relevance feedback-based query expansion by the use of a weighted word pairs approach (2015) 0.01 
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  5. Li, J.; Zhang, P.; Song, D.; Wu, Y.: Understanding an enriched multidimensional user relevance model by analyzing query logs (2017) 0.01 
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  6. Munkelt, J.; Schaer, P.; Lepsky, K.: Towards an IR test collection for the German National Library (2018) 0.01 
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  7. Iivonen, M.: Consistency in the selection of search concepts and search terms (1995) 0.01 
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    Abstract
    Considers intersearcher and intrasearcher consistency in the selection of search terms. Based on an empirical study where 22 searchers from 4 different types of search environments analyzed altogether 12 search requests of 4 different types in 2 separate test situations between which 2 months elapsed. Statistically very significant differences in consistency were found according to the types of search environments and search requests. Consistency was also considered according to the extent of the scope of search concept. At level I search terms were compared character by character. At level II different search terms were accepted as the same search concept with a rather simple evaluation of linguistic expressions. At level III, in addition to level II, the hierarchical approach of the search request was also controlled. At level IV different search terms were accepted as the same search concept with a broad interpretation of the search concept. Both intersearcher and intrasearcher consistency grew most immediately after a rather simple evaluation of linguistic impressions
  8. Sievert, M.E.; McKinin, E.J.: Why full-text misses some relevant documents : an analysis of documents not retrieved by CCML or MEDIS (1989) 0.01 
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    Date
    9. 1.1996 10:22:31
  9. Wood, F.; Ford, N.; Miller, D.; Sobczyk, G.; Duffin, R.: Information skills, searching behaviour and cognitive styles for student-centred learning : a computer-assisted learning approach (1996) 0.01 
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    Source
    Journal of information science. 22(1996) no.2, S.79-92
  10. Crestani, F.; Rijsbergen, C.J. van: Information retrieval by imaging (1996) 0.01 
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    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
  11. Leininger, K.: Interindexer consistency in PsychINFO (2000) 0.01 
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    Date
    9. 2.1997 18:44:22
  12. Kantor, P.; Kim, M.H.; Ibraev, U.; Atasoy, K.: Estimating the number of relevant documents in enormous collections (1999) 0.01 
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  13. Hansen, P.; Karlgren, J.: Effects of foreign language and task scenario on relevance assessment (2005) 0.01 
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  14. Mettrop, W.; Nieuwenhuysen, P.: Internet search engines : fluctuations in document accessibility (2001) 0.01 
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  15. Borlund, P.: Experimental components for the evaluation of interactive information retrieval systems (2000) 0.01 
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  16. Díaz, A.; García, A.; Gervás, P.: User-centred versus system-centred evaluation of a personalization system (2008) 0.01 
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  17. Vakkari, P.; Huuskonen, S.: Search effort degrades search output but improves task outcome (2012) 0.01 
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  18. Hider, P.: ¬The search value added by professional indexing to a bibliographic database (2017) 0.01 
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  19. Hider, P.: ¬The search value added by professional indexing to a bibliographic database (2018) 0.01 
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  20. Losada, D.E.; Parapar, J.; Barreiro, A.: When to stop making relevance judgments? : a study of stopping methods for building information retrieval test collections (2019) 0.01 
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    Abstract
    In information retrieval evaluation, pooling is a well-known technique to extract a sample of documents to be assessed for relevance. Given the pooled documents, a number of studies have proposed different prioritization methods to adjudicate documents for judgment. These methods follow different strategies to reduce the assessment effort. However, there is no clear guidance on how many relevance judgments are required for creating a reliable test collection. In this article we investigate and further develop methods to determine when to stop making relevance judgments. We propose a highly diversified set of stopping methods and provide a comprehensive analysis of the usefulness of the resulting test collections. Some of the stopping methods introduced here combine innovative estimates of recall with time series models used in Financial Trading. Experimental results on several representative collections show that some stopping methods can reduce up to 95% of the assessment effort and still produce a robust test collection. We demonstrate that the reduced set of judgments can be reliably employed to compare search systems using disparate effectiveness metrics such as Average Precision, NDCG, P@100, and Rank Biased Precision. With all these measures, the correlations found between full pool rankings and reduced pool rankings is very high.

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