Search (309 results, page 16 of 16)

  • × theme_ss:"Automatisches Indexieren"
  1. 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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    Source
    Information processing and management. 54(2018) no.6, S.1345-1358
  2. Wang, S.; Koopman, R.: Embed first, then predict (2019) 0.00 
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    Footnote
    Beitrag eines Special Issue: Research Information Systems and Science Classifications; including papers from "Trajectories for Research: Fathoming the Promise of the NARCIS Classification," 27-28 September 2018, The Hague, The Netherlands.
  3. Zhang, Y.; Zhang, C.; Li, J.: Joint modeling of characters, words, and conversation contexts for microblog keyphrase extraction (2020) 0.00 
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    Journal of the Association for Information Science and Technology. 71(2020) no.5, S.553-567
  4. Giesselbach, S.; Estler-Ziegler, T.: Dokumente schneller analysieren mit Künstlicher Intelligenz (2021) 0.00 
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    Footnote
    Vortrag im Rahmen des Berliner Arbeitskreis Information (BAK) am 25.02.2021.
  5. Villaespesa, E.; Crider, S.: ¬A critical comparison analysis between human and machine-generated tags for the Metropolitan Museum of Art's collection (2021) 0.00 
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    Abstract
    Purpose Based on the highlights of The Metropolitan Museum of Art's collection, the purpose of this paper is to examine the similarities and differences between the subject keywords tags assigned by the museum and those produced by three computer vision systems. Design/methodology/approach This paper uses computer vision tools to generate the data and the Getty Research Institute's Art and Architecture Thesaurus (AAT) to compare the subject keyword tags. Findings This paper finds that there are clear opportunities to use computer vision technologies to automatically generate tags that expand the terms used by the museum. This brings a new perspective to the collection that is different from the traditional art historical one. However, the study also surfaces challenges about the accuracy and lack of context within the computer vision results. Practical implications This finding has important implications on how these machine-generated tags complement the current taxonomies and vocabularies inputted in the collection database. In consequence, the museum needs to consider the selection process for choosing which computer vision system to apply to their collection. Furthermore, they also need to think critically about the kind of tags they wish to use, such as colors, materials or objects. Originality/value The study results add to the rapidly evolving field of computer vision within the art information context and provide recommendations of aspects to consider before selecting and implementing these technologies.
  6. Ahmed, M.: Automatic indexing for agriculture : designing a framework by deploying Agrovoc, Agris and Annif (2023) 0.00 
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    Source
    ¬SRELS Journal of Information Management. 60(2023) no.2, S.85-95
  7. Chung, E.-K.; Miksa, S.; Hastings, S.K.: ¬A framework of automatic subject term assignment for text categorization : an indexing conception-based approach (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.4, S.688-699
  8. Willis, C.; Losee, R.M.: ¬A random walk on an ontology : using thesaurus structure for automatic subject indexing (2013) 0.00 
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    Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1330-1344
  9. Maas, H.-D.: Indexieren mit AUTINDEX (2006) 0.00 
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    Source
    Information und Sprache: Beiträge zu Informationswissenschaft, Computerlinguistik, Bibliothekswesen und verwandten Fächern. Festschrift für Harald H. Zimmermann. Herausgegeben von Ilse Harms, Heinz-Dirk Luckhardt und Hans W. Giessen

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