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  • × theme_ss:"Retrievalalgorithmen"
  1. Khoo, C.S.G.; Wan, K.-W.: ¬A simple relevancy-ranking strategy for an interface to Boolean OPACs (2004) 0.06 
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    Abstract
    A relevancy-ranking algorithm for a natural language interface to Boolean online public access catalogs (OPACs) was formulated and compared with that currently used in a knowledge-based search interface called the E-Referencer, being developed by the authors. The algorithm makes use of seven weIl-known ranking criteria: breadth of match, section weighting, proximity of query words, variant word forms (stemming), document frequency, term frequency and document length. The algorithm converts a natural language query into a series of increasingly broader Boolean search statements. In a small experiment with ten subjects in which the algorithm was simulated by hand, the algorithm obtained good results with a mean overall precision of 0.42 and mean average precision of 0.62, representing a 27 percent improvement in precision and 41 percent improvement in average precision compared to the E-Referencer. The usefulness of each step in the algorithm was analyzed and suggestions are made for improving the algorithm.
    Source
    Electronic library. 22(2004) no.2, S.112-120
  2. Chen, Z.; Fu, B.: On the complexity of Rocchio's similarity-based relevance feedback algorithm (2007) 0.04 
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    Abstract
    Rocchio's similarity-based relevance feedback algorithm, one of the most important query reformation methods in information retrieval, is essentially an adaptive learning algorithm from examples in searching for documents represented by a linear classifier. Despite its popularity in various applications, there is little rigorous analysis of its learning complexity in literature. In this article, the authors prove for the first time that the learning complexity of Rocchio's algorithm is O(d + d**2(log d + log n)) over the discretized vector space {0, ... , n - 1 }**d when the inner product similarity measure is used. The upper bound on the learning complexity for searching for documents represented by a monotone linear classifier (q, 0) over {0, ... , n - 1 }d can be improved to, at most, 1 + 2k (n - 1) (log d + log(n - 1)), where k is the number of nonzero components in q. Several lower bounds on the learning complexity are also obtained for Rocchio's algorithm. For example, the authors prove that Rocchio's algorithm has a lower bound Omega((d über 2)log n) on its learning complexity over the Boolean vector space {0,1}**d.
  3. Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013) 0.04 
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    Abstract
    A new challenge, accessing multiple relevant entities, arises from the availability of linked heterogeneous data. In this article, we address more specifically the problem of accessing relevant entities, such as publications and authors within a bibliographic network, given an information need. We propose a novel algorithm, called BibRank, that estimates a joint relevance of documents and authors within a bibliographic network. This model ranks each type of entity using a score propagation algorithm with respect to the query topic and the structure of the underlying bi-type information entity network. Evidence sources, namely content-based and network-based scores, are both used to estimate the topical similarity between connected entities. For this purpose, authorship relationships are analyzed through a language model-based score on the one hand and on the other hand, non topically related entities of the same type are detected through marginal citations. The article reports the results of experiments using the Bibrank algorithm for an information retrieval task. The CiteSeerX bibliographic data set forms the basis for the topical query automatic generation and evaluation. We show that a statistically significant improvement over closely related ranking models is achieved.
    Date
    22. 3.2013 19:34:49
  4. 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.
  5. Sparck Jones, K.: ¬A statistical interpretation of term specifity and its application in retrieval (1972) 0.03 
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  6. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.03 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  7. Yu, K.; Tresp, V.; Yu, S.: ¬A nonparametric hierarchical Bayesian framework for information filtering (2004) 0.03 
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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
  8. Niemi, T.; Junkkari, M.; Järvelin, K.; Viita, S.: Advanced query language for manipulating complex entities (2004) 0.03 
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  9. Jones, K.: Linguistic searching versus relevance ranking : DR-LINK and TARGET (1999) 0.03 
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  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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.
  16. 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.
  17. 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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  18. Ding, Y.; Yan, E.; Frazho, A.; Caverlee, J.: PageRank for ranking authors in co-citation networks (2009) 0.02 
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    Abstract
    This paper studies how varied damping factors in the PageRank algorithm influence the ranking of authors and proposes weighted PageRank algorithms. We selected the 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculated the ranks of these 108 authors based on PageRank with the damping factor ranging from 0.05 to 0.95. In order to test the relationship between different measures, we compared PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank with different damping factors and also with different weighted PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index rank does not significantly correlate with centrality measures but does significantly correlate with other measures. The key factors that have impact on the PageRank of authors in the author co-citation network are being co-cited with important authors.
  19. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.02 
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    Date
    14. 6.2015 22:12:44
  20. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.02 
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    Date
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Authors

Years

Languages

  • e 68
  • d 7
  • m 1
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Types

  • a 65
  • m 7
  • s 3
  • el 1
  • r 1
  • x 1
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Themes

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Classifications