Search (31 results, page 1 of 2)

  • × theme_ss:"Literaturübersicht"
  1. Windfeld Lund, N.; Smalheiser, N.; Torvik, V.: Author name disambiguation (2009) 0.05
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  2. Kranich, N.; Schement, J.: Information commons (2008) 0.04
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  3. Wacholder, N.: Interactive query formulation (2011) 0.03
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  4. Caidi, N.; Allard, D.; Quirke, L.: Information practices of information (2010) 0.03
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  5. Enser, P.G.B.: Visual image retrieval (2008) 0.02
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    Date
    22. 1.2012 13:01:26
  6. Morris, S.A.: Mapping research specialties (2008) 0.02
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    Date
    13. 7.2008 9:30:22
  7. Fallis, D.: Social epistemology and information science (2006) 0.02
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    Date
    13. 7.2008 19:22:28
  8. Nicolaisen, J.: Citation analysis (2007) 0.02
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    Date
    13. 7.2008 19:53:22
  9. Metz, A.: Community service : a bibliography (1996) 0.02
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    Date
    17.10.1996 14:22:33
  10. Belkin, N.J.; Croft, W.B.: Retrieval techniques (1987) 0.02
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    Source
    Annual review of information science and technology. 22(1987), S.109-145
  11. Smith, L.C.: Artificial intelligence and information retrieval (1987) 0.02
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    Source
    Annual review of information science and technology. 22(1987), S.41-77
  12. Warner, A.J.: Natural language processing (1987) 0.02
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    Source
    Annual review of information science and technology. 22(1987), S.79-108
  13. Grudin, J.: Human-computer interaction (2011) 0.02
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    Date
    27.12.2014 18:54:22
  14. Liu, X.; Croft, W.B.: Statistical language modeling for information retrieval (2004) 0.02
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    Abstract
    This chapter reviews research and applications in statistical language modeling for information retrieval (IR), which has emerged within the past several years as a new probabilistic framework for describing information retrieval processes. Generally speaking, statistical language modeling, or more simply language modeling (LM), involves estimating a probability distribution that captures statistical regularities of natural language use. Applied to information retrieval, language modeling refers to the problem of estimating the likelihood that a query and a document could have been generated by the same language model, given the language model of the document either with or without a language model of the query. The roots of statistical language modeling date to the beginning of the twentieth century when Markov tried to model letter sequences in works of Russian literature (Manning & Schütze, 1999). Zipf (1929, 1932, 1949, 1965) studied the statistical properties of text and discovered that the frequency of works decays as a Power function of each works rank. However, it was Shannon's (1951) work that inspired later research in this area. In 1951, eager to explore the applications of his newly founded information theory to human language, Shannon used a prediction game involving n-grams to investigate the information content of English text. He evaluated n-gram models' performance by comparing their crossentropy an texts with the true entropy estimated using predictions made by human subjects. For many years, statistical language models have been used primarily for automatic speech recognition. Since 1980, when the first significant language model was proposed (Rosenfeld, 2000), statistical language modeling has become a fundamental component of speech recognition, machine translation, and spelling correction.
  15. Rader, H.B.: Library orientation and instruction - 1993 (1994) 0.02
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    Source
    Reference services review. 22(1994) no.4, S.81-
  16. Julien, C.-A.; Leide, J.E.; Bouthillier, F.: Controlled user evaluations of information visualization interfaces for text retrieval : literature review and meta-analysis (2008) 0.01
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    Abstract
    This review describes experimental designs (users, search tasks, measures, etc.) used by 31 controlled user studies of information visualization (IV) tools for textual information retrieval (IR) and a meta-analysis of the reported statistical effects. Comparable experimental designs allow research designers to compare their results with other reports, and support the development of experimentally verified design guidelines concerning which IV techniques are better suited to which types of IR tasks. The studies generally use a within-subject design with 15 or more undergraduate students performing browsing to known-item tasks on sets of at least 1,000 full-text articles or Web pages on topics of general interest/news. Results of the meta-analysis (N = 8) showed no significant effects of the IV tool as compared with a text-only equivalent, but the set shows great variability suggesting an inadequate basis of comparison. Experimental design recommendations are provided which would support comparison of existing IV tools for IR usability testing.
  17. Yu, N.: Readings & Web resources for faceted classification 0.01
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  18. Hsueh, D.C.: Recon road maps : retrospective conversion literature, 1980-1990 (1992) 0.01
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    Source
    Cataloging and classification quarterly. 14(1992) nos.3/4, S.5-22
  19. Gabbard, R.: Recent literature shows accelerated growth in hypermedia tools : an annotated bibliography (1994) 0.01
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    Source
    Reference services review. 22(1994) no.2, S.31-40
  20. Buckland, M.K.; Liu, Z.: History of information science (1995) 0.01
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    Date
    13. 6.1996 19:22:20