Search (5 results, page 1 of 1)

  • × language_ss:"e"
  • × theme_ss:"Automatisches Indexieren"
  • × year_i:[2020 TO 2030}
  1. Oliver, C.: Leveraging KOS to extend our reach with automated processes (2021) 0.01 
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    Abstract
    This article provides a conclusion to the special issue on Artificial Intelligence (AI) and Automated Processes for Subject Access. The authors who contributed to this special issue have provoked interesting questions as well as bringing attention to important issues. This concluding article looks at common themes and highlights some of the questions raised.
  2. Lowe, D.B.; Dollinger, I.; Koster, T.; Herbert, B.E.: Text mining for type of research classification (2021) 0.01 
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    Abstract
    This project brought together undergraduate students in Computer Science with librarians to mine abstracts of articles from the Texas A&M University Libraries' institutional repository, OAKTrust, in order to probe the creation of new metadata to improve discovery and use. The mining operation task consisted simply of classifying the articles into two categories of research type: basic research ("for understanding," "curiosity-based," or "knowledge-based") and applied research ("use-based"). These categories are fundamental especially for funders but are also important to researchers. The mining-to-classification steps took several iterations, but ultimately, we achieved good results with the toolkit BERT (Bidirectional Encoder Representations from Transformers). The project and its workflows represent a preview of what may lie ahead in the future of crafting metadata using text mining techniques to enhance discoverability.
  3. Asula, M.; Makke, J.; Freienthal, L.; Kuulmets, H.-A.; Sirel, R.: Kratt: developing an automatic subject indexing tool for the National Library of Estonia : how to transfer metadata information among work cluster members (2021) 0.01 
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  4. Chou, C.; Chu, T.: ¬An analysis of BERT (NLP) for assisted subject indexing for Project Gutenberg (2022) 0.01 
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    Abstract
    In light of AI (Artificial Intelligence) and NLP (Natural language processing) technologies, this article examines the feasibility of using AI/NLP models to enhance the subject indexing of digital resources. While BERT (Bidirectional Encoder Representations from Transformers) models are widely used in scholarly communities, the authors assess whether BERT models can be used in machine-assisted indexing in the Project Gutenberg collection, through suggesting Library of Congress subject headings filtered by certain Library of Congress Classification subclass labels. The findings of this study are informative for further research on BERT models to assist with automatic subject indexing for digital library collections.
  5. Ahmed, M.: Automatic indexing for agriculture : designing a framework by deploying Agrovoc, Agris and Annif (2023) 0.01 
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