ArgumenText: Argument Classification and Clustering in a Generalized Search Scenario
Author:
Funder
Technische Universität Darmstadt
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
http://link.springer.com/content/pdf/10.1007/s13222-020-00347-7.pdf
Reference27 articles.
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2. Ajjour Y, Wachsmuth H, Kiesel J, Potthast M, Hagen M, Stein B (2019) Data acquisition for argument search: The args.me corpus. In: Benzmüller C, Stuckenschmidt H (eds) KI 2019: advances in artificial intelligence. Springer, Heidelberg, Berlin, New York, pp 48–59
3. Boltužić F, Šnajder J (2015) Identifying prominent arguments in online debates using semantic textual similarity. In: ArgMining@NAACL-HLT’15, pp 110–115. https://doi.org/10.3115/v1/W15-0514
4. Chen S, Khashabi D, Callison-Burch C, Roth D (2019) PerspectroScope: a window to the world of diverse perspectives. In: ACL’19: System Demonstrations, pp 129–134. https://doi.org/10.18653/v1/P19-3022
5. Chernodub A, Oliynyk O, Heidenreich P, Bondarenko A, Hagen M, Biemann C, Panchenko A (2019) TARGER: neural argument mining at your fingertips. In: ACL’19: system demonstrations, pp 195–200. https://doi.org/10.18653/v1/P19-3031
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