Search (78 results, page 4 of 4)

  • × theme_ss:"Automatisches Indexieren"
  • × year_i:[2010 TO 2020}
  1. Vlachidis, A.; Tudhope, D.: ¬A knowledge-based approach to information extraction for semantic interoperability in the archaeology domain (2016) 0.00 
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    Journal of the Association for Information Science and Technology. 67(2016) no.5, S.1138-1152
  2. Husevag, A.-S.R.: Named entities in indexing : a case study of TV subtitles and metadata records (2016) 0.00 
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    Pages
    S.48-58
  3. Kanan, T.; Fox, E.A.: Automated arabic text classification with P-Stemmer, machine learning, and a tailored news article taxonomy (2016) 0.00 
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    Journal of the Association for Information Science and Technology. 67(2016) no.11, S.2667-2683
  4. Bredack, J.: Automatische Extraktion fachterminologischer Mehrwortbegriffe : ein Verfahrensvergleich (2016) 0.00 
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    Pages
    V, 98 S
  5. Blank, I.; Rokach, L.; Shani, G.: Leveraging metadata to recommend keywords for academic papers (2016) 0.00 
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    Journal of the Association for Information Science and Technology. 67(2016) no.12, S.3073-3091
  6. Golub, K.; Soergel, D.; Buchanan, G.; Tudhope, D.; Lykke, M.; Hiom, D.: ¬A framework for evaluating automatic indexing or classification in the context of retrieval (2016) 0.00 
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    Journal of the Association for Information Science and Technology. 67(2016) no.1, S.3-16
  7. Smiraglia, R.P.; Cai, X.: Tracking the evolution of clustering, machine learning, automatic indexing and automatic classification in knowledge organization (2017) 0.00 
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    Knowledge organization. 44(2017) no.3, S.215-233
  8. Ma, N.; Zheng, H.T.; Xiao, X.: ¬An ontology-based latent semantic indexing approach using long short-term memory networks (2017) 0.00 
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    Pages
    S.185-199
  9. Lu, K.; Mao, J.: ¬An automatic approach to weighted subject indexing : an empirical study in the biomedical domain (2015) 0.00 
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    Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1776-1784
  10. Li, X.; Zhang, A.; Li, C.; Ouyang, J.; Cai, Y.: Exploring coherent topics by topic modeling with term weighting (2018) 0.00 
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    Information processing and management. 54(2018) no.6, S.1345-1358
  11. Golub, K.: Automatic subject indexing of text (2019) 0.00 
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    Knowledge organization. 46(2019) no.2, S.104-121
  12. Pollmeier, M.: Verlagsschlagwörter als Grundlage für den Einsatz eines maschinellen Verfahrens zur verbalen Erschließung der Kinder- und Jugendliteratur durch die Deutsche Nationalbibliothek : eine Datenanalyse (2019) 0.00 
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    Pages
    66 S., CD-ROM
  13. Groß, T.; Faden, M.: Automatische Indexierung elektronischer Dokumente an der Deutschen Zentralbibliothek für Wirtschaftswissenschaften : Bericht über die Jahrestagung der Internationalen Buchwissenschaftlichen Gesellschaft (2010) 0.00 
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    Bibliotheksdienst. 44(2010) H.12, S.1120-1135
  14. Carevic, Z.: Semi-automatische Verschlagwortung zur Integration externer semantischer Inhalte innerhalb einer medizinischen Kooperationsplattform (2012) 0.00 
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    Pages
    99 S
  15. Tavakolizadeh-Ravari, M.: Analysis of the long term dynamics in thesaurus developments and its consequences (2017) 0.00 
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    128 S
  16. Bredack, J.: Terminologieextraktion von Mehrwortgruppen in kunsthistorischen Fachtexten (2013) 0.00 
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    Pages
    VI, 66 S
  17. Vinyals, O.; Toshev, A.; Bengio, S.; Erhan, D.: ¬A picture is worth a thousand (coherent) words : building a natural description of images (2014) 0.00 
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  18. Markoff, J.: Researchers announce advance in image-recognition software (2014) 0.00 
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    Content
    Computer vision specialists said that despite the improvements, these software systems had made only limited progress toward the goal of digitally duplicating human vision and, even more elusive, understanding. "I don't know that I would say this is 'understanding' in the sense we want," said John R. Smith, a senior manager at I.B.M.'s T.J. Watson Research Center in Yorktown Heights, N.Y. "I think even the ability to generate language here is very limited." But the Google and Stanford teams said that they expect to see significant increases in accuracy as they improve their software and train these programs with larger sets of annotated images. A research group led by Tamara L. Berg, a computer scientist at the University of North Carolina at Chapel Hill, is training a neural network with one million images annotated by humans. "You're trying to tell the story behind the image," she said. "A natural scene will be very complex, and you want to pick out the most important objects in the image.""

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