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  • × theme_ss:"Retrievalalgorithmen"
  • × type_ss:"a"
  1. Jacso, P.: Testing the calculation of a realistic h-index in Google Scholar, Scopus, and Web of Science for F. W. Lancaster (2008) 0.01
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  2. Pan, M.; Huang, J.X.; He, T.; Mao, Z.; Ying, Z.; Tu, X.: ¬A simple kernel co-occurrence-based enhancement for pseudo-relevance feedback (2020) 0.01
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    Abstract
    Pseudo-relevance feedback is a well-studied query expansion technique in which it is assumed that the top-ranked documents in an initial set of retrieval results are relevant and expansion terms are then extracted from those documents. When selecting expansion terms, most traditional models do not simultaneously consider term frequency and the co-occurrence relationships between candidate terms and query terms. Intuitively, however, a term that has a higher co-occurrence with a query term is more likely to be related to the query topic. In this article, we propose a kernel co-occurrence-based framework to enhance retrieval performance by integrating term co-occurrence information into the Rocchio model and a relevance language model (RM3). Specifically, a kernel co-occurrence-based Rocchio method (KRoc) and a kernel co-occurrence-based RM3 method (KRM3) are proposed. In our framework, co-occurrence information is incorporated into both the factor of the term discrimination power and the factor of the within-document term weight to boost retrieval performance. The results of a series of experiments show that our proposed methods significantly outperform the corresponding strong baselines over all data sets in terms of the mean average precision and over most data sets in terms of P@10. A direct comparison of standard Text Retrieval Conference data sets indicates that our proposed methods are at least comparable to state-of-the-art approaches.
  3. Burgin, R.: ¬The retrieval effectiveness of 5 clustering algorithms as a function of indexing exhaustivity (1995) 0.01
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    Date
    22. 2.1996 11:20:06
  4. Efthimiadis, E.N.: User choices : a new yardstick for the evaluation of ranking algorithms for interactive query expansion (1995) 0.01
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    Date
    22. 2.1996 13:14:10
  5. Song, D.; Bruza, P.D.: Towards context sensitive information inference (2003) 0.01
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    Date
    22. 3.2003 19:35:46
  6. Shiri, A.A.; Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment : a user-centered evaluation (2006) 0.01
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    Date
    22. 7.2006 16:32:43
  7. Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013) 0.01
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    Date
    22. 3.2013 19:34:49
  8. Khoo, C.S.G.; Wan, K.-W.: ¬A simple relevancy-ranking strategy for an interface to Boolean OPACs (2004) 0.01
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    Source
    Electronic library. 22(2004) no.2, S.112-120

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