Search (14 results, page 1 of 1)

  • × theme_ss:"Automatisches Indexieren"
  • × type_ss:"el"
  1. Schöneberg, U.; Gödert, W.: Erschließung mathematischer Publikationen mittels linguistischer Verfahren (2012) 0.05 
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    Source
    http://at.yorku.ca/c/b/f/j/99.htm
  2. 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) 0.02 
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    Date
    19. 8.2017 9:24:22
  3. Wiesenmüller, H.: DNB-Sacherschließung : Neues für die Reihen A und B (2019) 0.01 
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    Abstract
    "Alle paar Jahre wird die Bibliothekscommunity mit Veränderungen in der inhaltlichen Erschließung durch die Deutsche Nationalbibliothek konfrontiert. Sicher werden sich viele noch an die Einschnitte des Jahres 2014 für die Reihe A erinnern: Seither werden u.a. Ratgeber, Sprachwörterbücher, Reiseführer und Kochbücher nicht mehr mit Schlagwörtern erschlossen (vgl. das DNB-Konzept von 2014). Das Jahr 2017 brachte die Einführung der maschinellen Indexierung für die Reihen B und H bei gleichzeitigem Verlust der DDC-Tiefenerschließung (vgl. DNB-Informationen von 2017). Virulent war seither die Frage, was mit der Reihe A passieren würde. Seit wenigen Tagen kann man dies nun auf der Website der DNB nachlesen. (Nebenbei: Es ist zu befürchten, dass viele Links in diesem Blog-Beitrag in absehbarer Zeit nicht mehr funktionieren werden, da ein Relaunch der DNB-Website angekündigt ist. Wie beim letzten Mal wird es vermutlich auch diesmal keine Weiterleitungen von den alten auf die neuen URLs geben.)"
    Source
    https://www.basiswissen-rda.de/dnb-sacherschliessung-reihen-a-und-b/
  4. Peters, G.: Verschlagwortung und automatische Verfahren in der G+J Dokumentation (2003) 0.01 
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  5. Wiesenmüller, H.: Maschinelle Indexierung am Beispiel der DNB : Analyse und Entwicklungmöglichkeiten (2018) 0.01 
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    Abstract
    Der Beitrag untersucht die Ergebnisse des bei der Deutschen Nationalbibliothek (DNB) eingesetzten Verfahrens zur automatischen Vergabe von Schlagwörtern. Seit 2017 kommt dieses auch bei Printausgaben der Reihen B und H der Deutschen Nationalbibliografie zum Einsatz. Die zentralen Problembereiche werden dargestellt und an Beispielen illustriert - beispielsweise dass nicht alle im Inhaltsverzeichnis vorkommenden Wörter tatsächlich thematische Aspekte ausdrücken und dass die Software sehr häufig Körperschaften und andere "Named entities" nicht erkennt. Die maschinell generierten Ergebnisse sind derzeit sehr unbefriedigend. Es werden Überlegungen für mögliche Verbesserungen und sinnvolle Strategien angestellt.
  6. Mao, J.; Xu, W.; Yang, Y.; Wang, J.; Yuille, A.L.: Explain images with multimodal recurrent neural networks (2014) 0.01 
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  7. Kiros, R.; Salakhutdinov, R.; Zemel, R.S.: Unifying visual-semantic embeddings with multimodal neural language models (2014) 0.01 
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    Abstract
    Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. *image of a blue car* - "blue" + "red" is near images of red cars. Sample captions generated for 800 images are made available for comparison.
  8. Mielke, B.: Wider einige gängige Ansichten zur juristischen Informationserschließung (2002) 0.01 
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  9. Pielmeier, S.; Voß, V.; Carstensen, H.; Kahl, B.: Online-Workshop "Computerunterstützte Inhaltserschließung" 2020 (2021) 0.01 
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  10. Junger, U.: Can indexing be automated? : the example of the Deutsche Nationalbibliothek (2012) 0.01 
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  11. Gábor, K.; Zargayouna, H.; Tellier, I.; Buscaldi, D.; Charnois, T.: ¬A typology of semantic relations dedicated to scientific literature analysis (2016) 0.01 
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  12. 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) 0.01 
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  13. 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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  14. Markoff, J.: Researchers announce advance in image-recognition software (2014) 0.00 
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