Search (18 results, page 1 of 1)

  • × type_ss:"el"
  • × theme_ss:"Automatisches Indexieren"
  1. Junger, U.; Schwens, U.: ¬Die inhaltliche Erschließung des schriftlichen kulturellen Erbes auf dem Weg in die Zukunft : Automatische Vergabe von Schlagwörtern in der Deutschen Nationalbibliothek (2017) 0.01 
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    Date
    19. 8.2017 9:24:22
  2. Pielmeier, S.; Voß, V.; Carstensen, H.; Kahl, B.: Online-Workshop "Computerunterstützte Inhaltserschließung" 2020 (2021) 0.00 
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    Source
    o-bib: Das offene Bibliotheksjournal. 8(2021) Nr.1, S.1-10
  3. Franke-Maier, M.; Beck, C.; Kasprzik, A.; Maas, J.F.; Pielmeier, S.; Wiesenmüller, H: ¬Ein Feuerwerk an Algorithmen und der Startschuss zur Bildung eines Kompetenznetzwerks für maschinelle Erschließung : Bericht zur Fachtagung Netzwerk maschinelle Erschließung an der Deutschen Nationalbibliothek am 10. und 11. Oktober 2019 (2020) 0.00 
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    Source
    o-bib: Das offene Bibliotheksjournal. 7(2020) Nr.1, S.1-12
  4. Strobel, S.: Englischsprachige Erweiterung des TIB / AV-Portals : Ein GND/DBpedia-Mapping zur Gewinnung eines englischen Begriffssystems (2014) 0.00 
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    Source
    o-bib: Das offene Bibliotheksjournal. 1(2014) Nr.1, S.197-204
  5. Donahue, J.; Hendricks, L.A.; Guadarrama, S.; Rohrbach, M.; Venugopalan, S.; Saenko, K.; Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description (2014) 0.00 
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  6. Junger, U.: Can indexing be automated? : the example of the Deutsche Nationalbibliothek (2012) 0.00 
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    Source
    Cataloguing & Classification Quarterly 52(2014) no.1, S.102-109
  7. Wiesenmüller, H.: Maschinelle Indexierung am Beispiel der DNB : Analyse und Entwicklungmöglichkeiten (2018) 0.00 
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    Source
    o-bib: Das offene Bibliotheksjournal. 5(2018) Nr.4, S.141-153
  8. Mongin, L.; Fu, Y.Y.; Mostafa, J.: Open Archives data Service prototype and automated subject indexing using D-Lib archive content as a testbed (2003) 0.00 
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    Source
    D-Lib magazine. 9(2003) no.12, x S
  9. Mielke, B.: Wider einige gängige Ansichten zur juristischen Informationserschließung (2002) 0.00 
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    Pages
    S.273-287
  10. Beckmann, R.; Hinrichs, I.; Janßen, M.; Milmeister, G.; Schäuble, P.: ¬Der Digitale Assistent DA-3 : Eine Plattform für die Inhaltserschließung (2019) 0.00 
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    Source
    o-bib: Das offene Bibliotheksjournal. 6(2019) Nr.3, S.1-20
  11. Vinyals, O.; Toshev, A.; Bengio, S.; Erhan, D.: Show and tell : a neural image caption generator (2014) 0.00 
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  12. 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
  13. Giesselbach, S.; Estler-Ziegler, T.: Dokumente schneller analysieren mit Künstlicher Intelligenz (2021) 0.00 
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  14. Tavakolizadeh-Ravari, M.: Analysis of the long term dynamics in thesaurus developments and its consequences (2017) 0.00 
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    Pages
    128 S
  15. Kasprzik, A.: Aufbau eines produktiven Dienstes für die automatisierte Inhaltserschließung an der ZBW : ein Status- und Erfahrungsbericht. (2023) 0.00 
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    Source
    o-bib: Das offene Bibliotheksjournal. 10(2023) Nr.1, S.1-13
  16. 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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  17. Mödden, E.; Dreger, A.; Hommes, K.P.; Mohammadianbisheh, N.; Mölck, L.; Pinna, L.; Sitte-Zöllner, D.: ¬Der Weg zur Gründung der AG Erschließung ÖB-DNB und die Entwicklung eines maschinellen Verfahrens zur Verschlagwortung der Kinder- und Jugendliteratur mit GND-Vokabular (2020) 0.00 
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    o-bib: Das offene Bibliotheksjournal. 7(2020) Nr.4, S.1-9
  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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