Search (68 results, page 2 of 4)

  • × theme_ss:"Retrievalalgorithmen"
  1. Hüther, H.: Selix im DFG-Projekt Kascade (1998) 0.01 
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    Date
    25. 8.2000 19:55:29
  2. Kleinberg, J.M.: Authoritative sources in a hyperlinked environment (1998) 0.01 
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    Abstract
    The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of contexts on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of "authoritative" information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of "hub pages" that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristics for link-based analysis.
  3. Li, M.; Li, H.; Zhou, Z.-H.: Semi-supervised document retrieval (2009) 0.01 
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    Abstract
    This paper proposes a new machine learning method for constructing ranking models in document retrieval. The method, which is referred to as SSRank, aims to use the advantages of both the traditional Information Retrieval (IR) methods and the supervised learning methods for IR proposed recently. The advantages include the use of limited amount of labeled data and rich model representation. To do so, the method adopts a semi-supervised learning framework in ranking model construction. Specifically, given a small number of labeled documents with respect to some queries, the method effectively labels the unlabeled documents for the queries. It then uses all the labeled data to train a machine learning model (in our case, Neural Network). In the data labeling, the method also makes use of a traditional IR model (in our case, BM25). A stopping criterion based on machine learning theory is given for the data labeling process. Experimental results on three benchmark datasets and one web search dataset indicate that SSRank consistently and almost always significantly outperforms the baseline methods (unsupervised and supervised learning methods), given the same amount of labeled data. This is because SSRank can effectively leverage the use of unlabeled data in learning.
  4. Uratani, N.; Takeda, M.: ¬A fast string-searching algorithm for multiple patterns (1993) 0.01 
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    Source
    Information processing and management. 29(1993) no.6, S.775-791
  5. Chakrabarti, S.; Dom, B.; Kumar, S.R.; Raghavan, P.; Rajagopalan, S.; Tomkins, A.; Kleinberg, J.M.; Gibson, D.: Neue Pfade durch den Internet-Dschungel : Die zweite Generation von Web-Suchmaschinen (1999) 0.01 
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    Date
    31.12.1996 19:29:41
  6. Thompson, P.: Looking back: on relevance, probabilistic indexing and information retrieval (2008) 0.01 
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    Date
    31. 7.2008 19:58:29
  7. Vechtomova, O.; Karamuftuoglu, M.: Lexical cohesion and term proximity in document ranking (2008) 0.01 
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    Date
    1. 8.2008 12:29:05
  8. Maylein, L.; Langenstein, A.: Neues vom Relevanz-Ranking im HEIDI-Katalog der Universitätsbibliothek Heidelberg : Perspektiven für bibliothekarische Dienstleistungen (2013) 0.01 
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    Date
    29. 6.2013 18:06:23
  9. 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
  10. Faloutsos, C.: Signature files (1992) 0.01 
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    Date
    7. 5.1999 15:22:48
  11. 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
  12. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.01 
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    Date
    22. 8.2014 17:05:18
  13. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.01 
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    Date
    13. 9.2014 14:45:22
  14. Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015) 0.00 
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    Date
    20. 1.2015 18:30:22
    18. 9.2018 18:22:56
  15. Paris, L.A.H.; Tibbo, H.R.: Freestyle vs. Boolean : a comparison of partial and exact match retrieval systems (1998) 0.00 
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    Date
    12. 3.1999 10:29:27
  16. Otterbacher, J.; Erkan, G.; Radev, D.R.: Biased LexRank : passage retrieval using random walks with question-based priors (2009) 0.00 
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    Date
    22.11.2008 17:11:29
  17. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.00 
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    Date
    1. 8.1996 22:08:06
  18. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.00 
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    Date
    20. 3.2005 16:23:22
  19. Moffat, A.; Bell, T.A.H.: In situ generation of compressed inverted files (1995) 0.00 
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    Date
    27.11.1995 21:29:58
  20. Cannane, A.; Williams, H.E.: General-purpose compression for efficient retrieval (2001) 0.00 
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    Date
    29. 9.2001 13:59:55

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