Search (38 results, page 2 of 2)

  • × language_ss:"e"
  • × theme_ss:"Retrievalalgorithmen"
  1. Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004) 0.00 
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    Date
    16.11.2008 16:22:48
  2. Campos, L.M. de; Fernández-Luna, J.M.; Huete, J.F.: Implementing relevance feedback in the Bayesian network retrieval model (2003) 0.00 
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    Date
    22. 3.2003 19:30:19
  3. Ruthven, T.; Lalmas, M.; Rijsbergen, K.van: Incorporating user research behavior into relevance feedback (2003) 0.00 
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    Abstract
    Ruthven, Mounia, and van Rijsbergen rank and select terms for query expansion using information gathered on searcher evaluation behavior. Using the TREC Financial Times and Los Angeles Times collections and search topics from TREC-6 placed in simulated work situations, six student subjects each preformed three searches on an experimental system and three on a control system with instructions to search by natural language expression in any way they found comfortable. Searching was analyzed for behavior differences between experimental and control situations, and for effectiveness and perceptions. In three experiments paired t-tests were the analysis tool with controls being a no relevance feedback system, a standard ranking for automatic expansion system, and a standard ranking for interactive expansion while the experimental systems based ranking upon user information on temporal relevance and partial relevance. Two further experiments compare using user behavior (number assessed relevant and similarity of relevant documents) to choose a query expansion technique against a non-selective technique and finally the effect of providing the user with knowledge of the process. When partial relevance data and time of assessment data are incorporated in term ranking more relevant documents were recovered in fewer iterations, however retrieval effectiveness overall was not improved. The subjects, none-the-less, rated the suggested terms as more useful and used them more heavily. Explanations of what the feedback techniques were doing led to higher use of the techniques.
  4. Sakai, T.: On the reliability of information retrieval metrics based on graded relevance (2007) 0.00 
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  5. Deerwester, S.; Dumais, S.; Landauer, T.; Furnass, G.; Beck, L.: Improving information retrieval with latent semantic indexing (1988) 0.00 
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  6. Efron, M.: Linear time series models for term weighting in information retrieval (2010) 0.00 
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    Abstract
    Common measures of term importance in information retrieval (IR) rely on counts of term frequency; rare terms receive higher weight in document ranking than common terms receive. However, realistic scenarios yield additional information about terms in a collection. Of interest in this article is the temporal behavior of terms as a collection changes over time. We propose capturing each term's collection frequency at discrete time intervals over the lifespan of a corpus and analyzing the resulting time series. We hypothesize the collection frequency of a weakly discriminative term x at time t is predictable by a linear model of the term's prior observations. On the other hand, a linear time series model for a strong discriminators' collection frequency will yield a poor fit to the data. Operationalizing this hypothesis, we induce three time-based measures of term importance and test these against state-of-the-art term weighting models.
  7. Zhang, W.; Yoshida, T.; Tang, X.: ¬A comparative study of TF*IDF, LSI and multi-words for text classification (2011) 0.00 
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  8. Burgin, R.: ¬The retrieval effectiveness of 5 clustering algorithms as a function of indexing exhaustivity (1995) 0.00 
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    Date
    22. 2.1996 11:20:06
  9. Efthimiadis, E.N.: User choices : a new yardstick for the evaluation of ranking algorithms for interactive query expansion (1995) 0.00 
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    Date
    22. 2.1996 13:14:10
  10. Song, D.; Bruza, P.D.: Towards context sensitive information inference (2003) 0.00 
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    Date
    22. 3.2003 19:35:46
  11. Shiri, A.A.; Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment : a user-centered evaluation (2006) 0.00 
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    Date
    22. 7.2006 16:32:43
  12. Dominich, S.: Mathematical foundations of information retrieval (2001) 0.00 
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    Date
    22. 3.2008 12:26:32
  13. Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012) 0.00 
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    Date
    11. 6.2012 14:22:34
  14. Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013) 0.00 
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    Date
    22. 3.2013 19:34:49
  15. Lee, J.-T.; Seo, J.; Jeon, J.; Rim, H.-C.: Sentence-based relevance flow analysis for high accuracy retrieval (2011) 0.00 
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  16. Jacucci, G.; Barral, O.; Daee, P.; Wenzel, M.; Serim, B.; Ruotsalo, T.; Pluchino, P.; Freeman, J.; Gamberini, L.; Kaski, S.; Blankertz, B.: Integrating neurophysiologic relevance feedback in intent modeling for information retrieval (2019) 0.00 
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  17. 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) 0.00 
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  18. Khoo, C.S.G.; Wan, K.-W.: ¬A simple relevancy-ranking strategy for an interface to Boolean OPACs (2004) 0.00 
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    Source
    Electronic library. 22(2004) no.2, S.112-120

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