Search (28 results, page 1 of 2)

  • × language_ss:"e"
  • × theme_ss:"Retrievalalgorithmen"
  • × year_i:[2000 TO 2010}
  1. Zhang, D.; Dong, Y.: ¬An effective algorithm to rank Web resources (2000) 0.03 
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  2. 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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  3. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.01 
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    Date
    25. 8.2005 17:42:22
  4. Ding, Y.; Chowdhury, G.; Foo, S.: Organsising keywords in a Web search environment : a methodology based on co-word analysis (2000) 0.01 
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  5. Nie, J.-Y.: Query expansion and query translation as logical inference (2003) 0.01 
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  6. Xu, Y.; Wang, D.: Order effect in relevance judgment : mediation and causality (2008) 0.01 
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  7. Ding, Y.; Yan, E.; Frazho, A.; Caverlee, J.: PageRank for ranking authors in co-citation networks (2009) 0.01 
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  8. Sakai, T.: On the reliability of information retrieval metrics based on graded relevance (2007) 0.01 
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    Abstract
    This paper compares 14 information retrieval metrics based on graded relevance, together with 10 traditional metrics based on binary relevance, in terms of stability, sensitivity and resemblance of system rankings. More specifically, we compare these metrics using the Buckley/Voorhees stability method, the Voorhees/Buckley swap method and Kendall's rank correlation, with three data sets comprising test collections and submitted runs from NTCIR. Our experiments show that (Average) Normalised Discounted Cumulative Gain at document cut-off l are the best among the rank-based graded-relevance metrics, provided that l is large. On the other hand, if one requires a recall-based graded-relevance metric that is highly correlated with Average Precision, then Q-measure is the best choice. Moreover, these best graded-relevance metrics are at least as stable and sensitive as Average Precision, and are fairly robust to the choice of gain values.
  9. Zhao, L.; Wu, L.; Huang, X.: Using query expansion in graph-based approach for query-focused multi-document summarization (2009) 0.01 
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  10. Wei, F.; Li, W.; Lu, Q.; He, Y.: Applying two-level reinforcement ranking in query-oriented multidocument summarization (2009) 0.01 
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  11. Boughanem, M.; Chrisment, C.; Tamine, L.: On using genetic algorithms for multimodal relevance optimization in information retrieval (2002) 0.01 
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  12. Losee, R.M.; Church Jr., L.: Are two document clusters better than one? : the cluster performance question for information retrieval (2005) 0.01 
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  13. 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
  14. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.01 
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    Date
    22. 3.2003 19:27:23
  15. Thelwall, M.; Vaughan, L.: New versions of PageRank employing alternative Web document models (2004) 0.01 
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  16. Yang, L.; Ji, D.; Leong, M.: Document reranking by term distribution and maximal marginal relevance for chinese information retrieval (2007) 0.01 
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  17. Singh, S.; Dey, L.: ¬A rough-fuzzy document grading system for customized text information retrieval (2005) 0.01 
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  18. Crestani, F.; Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Mathematical, logical, and formal methods in information retrieval : an introduction to the special issue (2003) 0.01 
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    Date
    22. 3.2003 19:27:36
  19. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.01 
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    Date
    31. 5.2004 19:22:06
  20. Furner, J.: ¬A unifying model of document relatedness for hybrid search engines (2003) 0.01 
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    Date
    11. 9.2004 17:32:22

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