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    Robust Argumentation Machines

    First International Conference, RATIO 2024, Bielefeld, Germany, June 5–7, 2024, Proceedings

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    Contributor(s)
    Cimiano, Philipp (editor)
    Frank, Anette (editor)
    Kohlhase, Michael (editor)
    Stein, Benno (editor)
    Collection
    DFG - German Research Foundation
    Language
    English
    Show full item record
    Abstract
    This open access book constitutes the proceedings of the First International Conference on Robust Argumentation Machines, RATIO 2024, which took place in Bielefeld, Germany, during June 5-7, 2024. The 20 full papers and 1 short paper included in the proceedings were carefully reviewed and selected from 24 submissions. They were organized in topical sections as follows: Argument Mining; Debate Analysis and Deliberation; Argument Acquisition, Annotation and Quality Assessment; Computational Models of Argumentation; Interactive Argumentation, Recommendation and Personalization; and Argument Search and Retrieval.
    URI
    https://library.oapen.org/handle/20.500.12657/92695
    Keywords
    argument mining; computational agrumentation; deliberation support; argument search and retrieval; human computer interaction; natural language processing; knowledge representation; artificial intelligence
    DOI
    10.1007/978-3-031-63536-6
    ISBN
    9783031635366, 9783031635359, 9783031635366
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Cham, 2024
    Grantor
    • Deutsche Forschungsgemeinschaft - [...]
    Imprint
    Springer Nature Switzerland
    Series
    Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence, 14638
    Classification
    Artificial intelligence
    Mathematical theory of computation
    Mathematical logic
    Mathematical foundations
    Software Engineering
    Pages
    372
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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