Search (48 results, page 2 of 3)

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  1. 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
  2. Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012) 0.01
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    Date
    11. 6.2012 14:22:34
  3. 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
  4. Chen, Z.; Fu, B.: On the complexity of Rocchio's similarity-based relevance feedback algorithm (2007) 0.01
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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.
  5. Zhang, D.; Dong, Y.: ¬An effective algorithm to rank Web resources (2000) 0.01
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  6. Savoy, J.; Ndarugendamwo, M.; Vrajitoru, D.: Report on the TREC-4 experiment : combining probabilistic and vector-space schemes (1996) 0.01
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  7. White, H. D.: Co-cited author retrieval and relevance theory : examples from the humanities (2015) 0.01
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  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
  9. Dang, E.K.F.; Luk, R.W.P.; Allan, J.; Ho, K.S.; Chung, K.F.L.; Lee, D.L.: ¬A new context-dependent term weight computed by boost and discount using relevance information (2010) 0.00
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    Abstract
    We studied the effectiveness of a new class of context-dependent term weights for information retrieval. Unlike the traditional term frequency-inverse document frequency (TF-IDF), the new weighting of a term t in a document d depends not only on the occurrence statistics of t alone but also on the terms found within a text window (or "document-context") centered on t. We introduce a Boost and Discount (B&D) procedure which utilizes partial relevance information to compute the context-dependent term weights of query terms according to a logistic regression model. We investigate the effectiveness of the new term weights compared with the context-independent BM25 weights in the setting of relevance feedback. We performed experiments with title queries of the TREC-6, -7, -8, and 2005 collections, comparing the residual Mean Average Precision (MAP) measures obtained using B&D term weights and those obtained by a baseline using BM25 weights. Given either 10 or 20 relevance judgments of the top retrieved documents, using the new term weights yields improvement over the baseline for all collections tested. The MAP obtained with the new weights has relative improvement over the baseline by 3.3 to 15.2%, with statistical significance at the 95% confidence level across all four collections.
  10. Bodoff, D.; Enache, D.; Kambil, A.; Simon, G.; Yukhimets, A.: ¬A unified maximum likelihood approach to document retrieval (2001) 0.00
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  11. Harman, D.; Fox, E.; Baeza-Yates, R.; Lee, W.: Inverted files (1992) 0.00
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  12. Harman, D.: Relevance feedback and other query modification techniques (1992) 0.00
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  13. Harman, D.: Ranking algorithms (1992) 0.00
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  14. Wilbur, W.J.: ¬A retrieval system based on automatic relevance weighting of search terms (1992) 0.00
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    Source
    Proceedings of the 55th Annual Meeting of the American Society for Information Science, Pittsburgh, 26.-29.10.92. Ed.: D. Shaw
  15. Rada, R.; Barlow, J.; Potharst, J.; Zanstra, P.; Bijstra, D.: Document ranking using an enriched thesaurus (1991) 0.00
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  16. Bodoff, D.; Robertson, S.: ¬A new unified probabilistic model (2004) 0.00
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  17. Beitzel, S.M.; Jensen, E.C.; Chowdhury, A.; Grossman, D.; Frieder, O; Goharian, N.: Fusion of effective retrieval strategies in the same information retrieval system (2004) 0.00
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  18. Radev, D.; Fan, W.; Qu, H.; Wu, H.; Grewal, A.: Probabilistic question answering on the Web (2005) 0.00
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  19. Bodoff, D.; Wong, S.P.-S.: Documents and queries as random variables : history and implications (2006) 0.00
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  20. Yang, L.; Ji, D.; Leong, M.: Document reranking by term distribution and maximal marginal relevance for chinese information retrieval (2007) 0.00
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Years