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  • × theme_ss:"Retrievalstudien"
  1. Kluck, M.; Winter, M.: Topic-Entwicklung und Relevanzbewertung bei GIRT : ein Werkstattbericht (2006) 0.02 
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    Year
    2006
  2. Mandl, T.: Neue Entwicklungen bei den Evaluierungsinitiativen im Information Retrieval (2006) 0.02 
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  3. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.02 
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    Date
    20.10.2000 12:22:23
  4. 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
  5. 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
  6. 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
  7. Sun, Y.; Kantor, P.B.: Cross-evaluation : a new model for information system evaluation (2006) 0.02 
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    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.5, S.614-628
    Year
    2006
  8. Talvensaari, T.; Laurikkala, J.; Järvelin, K.; Juhola, M.: ¬A study on automatic creation of a comparable document collection in cross-language information retrieval (2006) 0.02 
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    Source
    Journal of documentation. 62(2006) no.3, S.372-387
    Year
    2006
  9. Allan, J.; Callan, J.P.; Croft, W.B.; Ballesteros, L.; Broglio, J.; Xu, J.; Shu, H.: INQUERY at TREC-5 (1997) 0.02 
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    Date
    27. 2.1999 20:55:22
  10. 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) 0.02 
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    Date
    27. 2.1999 20:59:22
  11. Saracevic, T.: On a method for studying the structure and nature of requests in information retrieval (1983) 0.02 
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    Pages
    S.22-25
  12. Lioma, C.; Ounis, I.: ¬A syntactically-based query reformulation technique for information retrieval (2008) 0.01 
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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.
  13. Rijsbergen, C.J. van: ¬A test for the separation of relevant and non-relevant documents in experimental retrieval collections (1973) 0.01 
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    Date
    19. 3.1996 11:22:12
  14. Sanderson, M.: ¬The Reuters test collection (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
  15. Lespinasse, K.: TREC: une conference pour l'evaluation des systemes de recherche d'information (1997) 0.01 
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    Date
    1. 8.1996 22:01:00
  16. ¬The Fifth Text Retrieval Conference (TREC-5) (1997) 0.01 
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    Abstract
    Proceedings of the 5th TREC-confrerence held in Gaithersburgh, Maryland, Nov 20-22, 1996. Aim of the conference was discussion on retrieval techniques for large test collections. Different research groups used different techniques, such as automated thesauri, term weighting, natural language techniques, relevance feedback and advanced pattern matching, for information retrieval from the same large database. This procedure makes it possible to compare the results. The proceedings include papers, tables of the system results, and brief system descriptions including timing and storage information
  17. Pemberton, J.K.; Ojala, M.; Garman, N.: Head to head : searching the Web versus traditional services (1998) 0.01 
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    Source
    Online. 22(1998) no.3, S.24-26,28
  18. Dresel, R.; Hörnig, D.; Kaluza, H.; Peter, A.; Roßmann, A.; Sieber, W.: Evaluation deutscher Web-Suchwerkzeuge : Ein vergleichender Retrievaltest (2001) 0.01 
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    Abstract
    Die deutschen Suchmaschinen, Abacho, Acoon, Fireball und Lycos sowie die Web-Kataloge Web.de und Yahoo! werden einem Qualitätstest nach relativem Recall, Precision und Availability unterzogen. Die Methoden der Retrievaltests werden vorgestellt. Im Durchschnitt werden bei einem Cut-Off-Wert von 25 ein Recall von rund 22%, eine Precision von knapp 19% und eine Verfügbarkeit von 24% erreicht
  19. Ellis, D.: Progress and problems in information retrieval (1996) 0.01 
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    Date
    26. 7.2002 20:22:46
  20. ¬The Eleventh Text Retrieval Conference, TREC 2002 (2003) 0.01 
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    Abstract
    Proceedings of the llth TREC-conference held in Gaithersburg, Maryland (USA), November 19-22, 2002. Aim of the conference was discussion an retrieval and related information-seeking tasks for large test collection. 93 research groups used different techniques, for information retrieval from the same large database. This procedure makes it possible to compare the results. The tasks are: Cross-language searching, filtering, interactive searching, searching for novelty, question answering, searching for video shots, and Web searching.

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