FAME: A Framework for Argument Mining and Evaluation

• Antragsteller: Professor Dr. Ringo Baumann; Professor Dr. Gerhard Heyer; Dr.-Ing. Gregor Wiedemann
• Fachliche Zuordnung: Bild- und Sprachverarbeitung, Computergraphik und Visualisierung, Human Computer Interaction, Ubiquitous und Wearable Computing ; Datenmanagement, datenintensive Systeme, Informatik-Methoden in der Wirtschaftsinformatik ; Theoretische Informatik
• Förderung: Förderung von 2019 bis 2023
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 406289255

• Erstellungsjahr: 2025

Keine Zusammenfassung vorhanden

Projektbezogene Publikationen (Auswahl)

• If Nothing Is Accepted -- Repairing Argumentation Frameworks. Journal of Artificial Intelligence Research, 66, 1099-1145.
  Ulbricht, Markus & Baumann, Ringo
  
• A Python Script for Abstract Dialectical Frameworks. SAFA@COMMA 2020: 74-79
  Baumann R. & Heinrich M.
  
• Comparing Weak Admissibility Semantics to their Dung-style Counterparts -- Reduct, Modularization, and Strong Equivalence in Abstract Argumentation. Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning, 79-88. International Joint Conferences on Artificial Intelligence Organization.
  Baumann, Ringo; Brewka, Gerhard & Ulbricht, Markus
  
• Revisiting the Foundations of Abstract Argumentation – Semantics Based on Weak Admissibility and Weak Defense. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2742-2749.
  Baumann, Ringo; Brewka, Gerhard & Ulbricht, Markus
  
• The Road Map to FAME: A Framework for Mining and Formal Evaluation of Arguments. Datenbank-Spektrum, 20(2), 107-113.
  Baumann, Ringo; Wiedemann, Gregor; Heinrich, Maximilian; Hakimi, Ahmad Dawar & Heyer, Gerhard
  
• Timed Abstract Dialectical Frameworks: A Simple Translation-Based Approach. Frontiers in Artificial Intelligence and Applications. IOS Press.
  Baumann Ringo & Heinrich Maximilian
  
• Comparing Weak Admissibility Semantics to their Dung-style Counterparts (Extended Abstract). Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 4740-4744. International Joint Conferences on Artificial Intelligence Organization.
  Baumann, Ringo; Brewka, Gerhard & Ulbricht, Markus
  
• On Classifying whether Two Texts are on the Same Side of an Argument. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 10130-10138. Association for Computational Linguistics.
  Körner, Erik; Wiedemann, Gregor; Hakimi, Ahmad Dawar; Heyer, Gerhard & Potthast, Martin
  
• On Cycles, Attackers and Supporters --- A Contribution to The Investigation of Dynamics in Abstract Argumentation. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 1780-1786. International Joint Conferences on Artificial Intelligence Organization.
  Baumann, Ringo & Ulbricht, Markus
  
• Argument Aspect Corpus - Nuclear Energy [Dataset]
  Jurkschat L., Wiedemann G., Heinrich M., Ruckdeschel M. & Torge S.
  
• Boundary Detection and Categorization of Argument Aspects via Supervised Learning. In: Proceedings of the 9th Workshop on Argument Mining. International Conference on Computational Linguistics; 2022:126-136
  Ruckdeschel M. & Wiedemann G.
  
• Few-shot learning for argument aspects of the nuclear energy debate. In: Proceedings of the Language Resources and Evaluation Conference. European Language Resources Association
  Jurkschat L., Wiedemann G., Heinrich M., Ruckdeschel M. & Torge S.
  
• Argument Aspect Corpus [Dataset]
  Ruckdeschel M. & Wiedemann G.
  
• Bipolar Abstract Dialectical Frameworks Are Covered by Kleene’s Three-valued Logic. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 3123-3131. International Joint Conferences on Artificial Intelligence Organization.
  Baumann, Ringo & Heinrich, Maximilian
  
• Argument Mining of Attack and Support Patterns in Dialogical Conversations with Sequential Pattern Mining. Lecture Notes in Computer Science, 39-56. Springer Nature Switzerland.
  Ruckdeschel, Mattes; Baumann, Ringo & Wiedemann, Gregor
  
• Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine. Studies in Communication and Media, 13(1), 72-100.
  Rieger, Jonas; Yanchenko, Kostiantyn; Ruckdeschel, Mattes; von Nordheim, Gerret; Kleinen-von Königslöw, Katharina & Wiedemann, Gregor