Search (52 results, page 1 of 3)

  • × theme_ss:"Retrievalalgorithmen"
  • × language_ss:"e"
  1. Sparck Jones, K.: Search term relevance weighting given little relevance information (1979) 0.03 
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.329-338.
  2. Sparck Jones, K.: ¬A statistical interpretation of term specifity and its application in retrieval (1972) 0.03 
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  3. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.02 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  4. Yu, K.; Tresp, V.; Yu, S.: ¬A nonparametric hierarchical Bayesian framework for information filtering (2004) 0.02 
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    Source
    SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a
  5. Niemi, T.; Junkkari, M.; Järvelin, K.; Viita, S.: Advanced query language for manipulating complex entities (2004) 0.02 
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  6. Jones, K.: Linguistic searching versus relevance ranking : DR-LINK and TARGET (1999) 0.02 
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  7. Khoo, C.S.G.; Wan, K.-W.: ¬A simple relevancy-ranking strategy for an interface to Boolean OPACs (2004) 0.02 
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    Source
    Electronic library. 22(2004) no.2, S.112-120
  8. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.02 
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    Date
    30. 3.2001 13:32:22
  9. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.02 
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    Date
    25. 8.2005 17:42:22
  10. Clarke, C.L.A.; Cormack, G.V.; Burkowski, F.J.: Shortest substring ranking : multitext experiments for TREC-4 (1996) 0.02 
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    Source
    The Fourth Text Retrieval Conference (TREC-4). Ed.: K. Harman
  11. Savoy, J.; Ndarugendamwo, M.; Vrajitoru, D.: Report on the TREC-4 experiment : combining probabilistic and vector-space schemes (1996) 0.02 
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    Source
    The Fourth Text Retrieval Conference (TREC-4). Ed.: K. Harman
  12. Belkin, N.J.; Cool, C.; Koenemann, J.; Ng, K.B.; Park, S.: Using relevance feedback and ranking in interactive searching (1996) 0.02 
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    Source
    The Fourth Text Retrieval Conference (TREC-4). Ed.: K. Harman
  13. Robertson, S.E.: ¬The probability ranking principle in IR (1977) 0.02 
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willet. San Francisco: Morgan Kaufmann 1997. S.281-286.
  14. Salton, G.; Buckley, C.: Term-weighting approaches in automatic text retrieval (1988) 0.02 
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.323-328.
  15. Okada, M.; Ando, K.; Lee, S.S.; Hayashi, Y.; Aoe, J.I.: ¬An efficient substring search method by using delayed keyword extraction (2001) 0.02 
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  16. Liu, A.; Zou, Q.; Chu, W.W.: Configurable indexing and ranking for XML information retrieval (2004) 0.02 
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    Source
    SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a
  17. Sparck Jones, K.: IDF term weighting and IR research lessons (2004) 0.02 
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  18. Lee, J.; Min, J.-K.; Oh, A.; Chung, C.-W.: Effective ranking and search techniques for Web resources considering semantic relationships (2014) 0.01 
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    Abstract
    On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved. In this paper, we present effective ranking and search techniques considering the semantic relationships in an ontology. Our technique retrieves top-k resources which are the most relevant to query keywords through the semantic relationships. To do this, we propose a weighting measure for the semantic relationship. Based on this measure, we propose a novel ranking method which considers the number of meaningful semantic relationships between a resource and keywords as well as the coverage and discriminating power of keywords. In order to improve the efficiency of the search, we prune the unnecessary search space using the length and weight thresholds of the semantic relationship path. In addition, we exploit Threshold Algorithm based on an extended inverted index to answer top-k results efficiently. The experimental results using real data sets demonstrate that our retrieval method using the semantic information generates accurate results efficiently compared to the traditional methods.
  19. Efron, M.; Winget, M.: Query polyrepresentation for ranking retrieval systems without relevance judgments (2010) 0.01 
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    Abstract
    Ranking information retrieval (IR) systems with respect to their effectiveness is a crucial operation during IR evaluation, as well as during data fusion. This article offers a novel method of approaching the system-ranking problem, based on the widely studied idea of polyrepresentation. The principle of polyrepresentation suggests that a single information need can be represented by many query articulations-what we call query aspects. By skimming the top k (where k is small) documents retrieved by a single system for multiple query aspects, we collect a set of documents that are likely to be relevant to a given test topic. Labeling these skimmed documents as putatively relevant lets us build pseudorelevance judgments without undue human intervention. We report experiments where using these pseudorelevance judgments delivers a rank ordering of IR systems that correlates highly with rankings based on human relevance judgments.
  20. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.01 
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    Date
    20. 1.2007 18:30:22

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