Search (121 results, page 1 of 7)

  • × theme_ss:"Retrievalstudien"
  1. Strzalkowski, T.; Guthrie, L.; Karlgren, J.; Leistensnider, J.; Lin, F.; Perez-Carballo, J.; Straszheim, T.; Wang, J.; Wilding, J.: Natural language information retrieval : TREC-5 report (1997) 0.06
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  2. Tomaiuolo, N.G.; Parker, J.: Maximizing relevant retrieval : keyword and natural language searching (1998) 0.05
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    Source
    Online. 22(1998) no.6, S.57-58
  3. Allan, J.; Callan, J.P.; Croft, W.B.; Ballesteros, L.; Broglio, J.; Xu, J.; Shu, H.: INQUERY at TREC-5 (1997) 0.05
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    Date
    27. 2.1999 20:55:22
  4. Saracevic, T.: On a method for studying the structure and nature of requests in information retrieval (1983) 0.05
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    Pages
    S.22-25
    Source
    Productivity in the information age : proceedings of the 46th ASIS annual meeting, 1983. Ed.: Raymond F Vondra
  5. Chevallet, J.-P.; Bruandet, M.F.: Impact de l'utilisation de multi terms sur la qualité des résponses dùn système de recherche d'information a indexation automatique (1999) 0.04
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    Language
    f
    Source
    Organisation des connaissances en vue de leur intégration dans les systèmes de représentation et de recherche d'information. Ed.: J. Maniez, et al
  6. Lespinasse, K.: TREC: une conference pour l'evaluation des systemes de recherche d'information (1997) 0.04
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    Date
    1. 8.1996 22:01:00
    Language
    f
  7. ¬The Second Text Retrieval Conference : TREC-2 (1995) 0.03
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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
  8. López-Ostenero, F.; Peinado, V.; Gonzalo, J.; Verdejo, F.: Interactive question answering : Is Cross-Language harder than monolingual searching? (2008) 0.03
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  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) 0.03
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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) 0.03
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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. Sievert, M.E.; McKinin, E.J.: Why full-text misses some relevant documents : an analysis of documents not retrieved by CCML or MEDIS (1989) 0.02
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    Date
    9. 1.1996 10:22:31
    Source
    ASIS'89. Managing information and technology. Proceedings of the 52nd annual meeting of the American Society for Information Science, Washington D.C., 30.10.-2.11.1989. Vol.26. Ed.by J. Katzer and G.B. Newby
  12. Reichert, S.; Mayr, P.: Untersuchung von Relevanzeigenschaften in einem kontrollierten Eyetracking-Experiment (2012) 0.02
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    Content
    Vgl.: http://www.degruyter.com/view/j/iwp.2012.63.issue-3/iwp-2012-0029/iwp-2012-0029.xml?format=INT. Vgl. auch: http://www.ib.hu-berlin.de/~mayr/arbeiten/iwp-2012-final.pdf.
    Date
    22. 7.2012 19:25:54
  13. Pal, S.; Mitra, M.; Kamps, J.: Evaluation effort, reliability and reusability in XML retrieval (2011) 0.02
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    Date
    22. 1.2011 14:20:56
  14. Lioma, C.; Ounis, I.: ¬A syntactically-based query reformulation technique for information retrieval (2008) 0.02
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    Abstract
    Whereas in language words of high frequency are generally associated with low content [Bookstein, A., & Swanson, D. (1974). Probabilistic models for automatic indexing. Journal of the American Society of Information Science, 25(5), 312-318; Damerau, F. J. (1965). An experiment in automatic indexing. American Documentation, 16, 283-289; Harter, S. P. (1974). A probabilistic approach to automatic keyword indexing. PhD thesis, University of Chicago; Sparck-Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28, 11-21; Yu, C., & Salton, G. (1976). Precision weighting - an effective automatic indexing method. Journal of the Association for Computer Machinery (ACM), 23(1), 76-88], shallow syntactic fragments of high frequency generally correspond to lexical fragments of high content [Lioma, C., & Ounis, I. (2006). Examining the content load of part of speech blocks for information retrieval. In Proceedings of the international committee on computational linguistics and the association for computational linguistics (COLING/ACL 2006), Sydney, Australia]. We implement this finding to Information Retrieval, as follows. We present a novel automatic query reformulation technique, which is based on shallow syntactic evidence induced from various language samples, and used to enhance the performance of an Information Retrieval system. Firstly, we draw shallow syntactic evidence from language samples of varying size, and compare the effect of language sample size upon retrieval performance, when using our syntactically-based query reformulation (SQR) technique. Secondly, we compare SQR to a state-of-the-art probabilistic pseudo-relevance feedback technique. Additionally, we combine both techniques and evaluate their compatibility. We evaluate our proposed technique across two standard Text REtrieval Conference (TREC) English test collections, and three statistically different weighting models. Experimental results suggest that SQR markedly enhances retrieval performance, and is at least comparable to pseudo-relevance feedback. Notably, the combination of SQR and pseudo-relevance feedback further enhances retrieval performance considerably. These collective experimental results confirm the tenet that high frequency shallow syntactic fragments correspond to content-bearing lexical fragments.
  15. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.01
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    Date
    20.10.2000 12:22:23
  16. Voorhees, E.M.; Harman, D.: Overview of the Sixth Text REtrieval Conference (TREC-6) (2000) 0.01
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    Date
    11. 8.2001 16:22:19
  17. Dalrymple, P.W.: Retrieval by reformulation in two library catalogs : toward a cognitive model of searching behavior (1990) 0.01
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    Date
    22. 7.2006 18:43:54
  18. Allan, J.; Croft, W.B.; Callan, J.: ¬The University of Massachusetts and a dozen TRECs (2005) 0.01
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  19. Crestani, F.; Ruthven, I.; Sanderson, M.; Rijsbergen, C.J. van: ¬The troubles with using a logical model of IR on a large collection of documents : experimenting retrieval by logical imaging on TREC (1996) 0.01
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  20. Callan, J.; Croft, W.B.; Broglio, J.: TREC and TIPSTER experiments with INQUERY (1995) 0.01
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Languages

  • e 101
  • d 16
  • f 2
  • chi 1
  • More… Less…

Types

  • a 111
  • s 4
  • m 3
  • el 2
  • p 2
  • r 1
  • x 1
  • More… Less…