Dr. Jonathan Kobbe
Credit: Emilie Orgler
Researcher
B6, 26, Room C 1.03
jonathanuni-mannheim.de
Project
Teachings
As I originally studied to become a teacher in Math and Computer Science, I naturally have a high interest in teaching activities. Thus, I held tutorials or exercises for the following lectures (recent to old):
Industrial Applications of Artificial Intelligence, Network Analysis, Business Informatics II: Foundations of Modeling, Artificial Intelligence, Algorithms and Data Structures, Formal Foundations of Computer Science, Programming Lab II, Analysis I, Programming in C
Research: Video Presentations & Posters
Unsupervised Stance Detection for Arguments from Consequences
Exploring Morality in Argumentation
Knowledge Graphs meet Moral Values
Effect Graph: Effect Relation Extraction for Explanation Generaiton
Publications
- Kobbe, J. (2023). Automatic generation of structured explanations for arguments from consequences. Dissertation. Mannheim.
- Kobbe, J., Hulpus, I. and Stuckenschmidt, H. (2023). Effect graph: Effect relation extraction for explanation generation. In , Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE) (S. 116–127). , Association for Computational Linguistics: Toronto, Canada.
- Becker, M., Hulpus, I., Opitz, J., Paul, D., Kobbe, J., Stuckenschmidt, H. and Frank, A. (2020). Explaining arguments with background knowledge : Towards knowledge-based argumentation analysis. Datenbank-Spektrum, 20, 131–141.
- Debjit, P., Opitz, J., Becker, M., Kobbe, J., Hirst, G. and Frank, A. (2020). Argumentative relation classification with background knowledge. In , Computational models of argument : proceedings of COMMA 2020 (S. 319–330). Frontiers in Artificial Intelligence and Applications, IOS Press: Amsterdam.
- Hulpus, I., Kobbe, J., Stuckenschmidt, H. and Hirst, G. (2020). Knowledge graphs meet moral values. In , Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics : Barcelona, Spain (Online), December 2020 (S. 71–80). , Association for Computational Linguistics: Stroudsburg, PA.
- Kobbe, J., Hulpus, I. and Stuckenschmidt, H. (2020). Unsupervised stance detection for arguments from consequences. In , EMNLP 2020 : proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 16th – 20th November 2020 (S. 50–60). , Association for Computational Linguistics: Online.
- Kobbe, J., Rehbein, I., Hulpus, I. and Stuckenschmidt, H. (2020). Exploring morality in argumentation. In , Proceedings of the 7th Workshop on Argument Mining : Barcelona, Spain (Online), December 13, 2020 (S. 30–40). , Association for Computational Linguistics, ACL: Stroudsburg, PA.
- Hulpus, I., Kobbe, J., Becker, M., Opitz, J., Hirst, G., Meilicke, C., Nastase, V., Stuckenschmidt, H. and Frank, A. (2019). Towards explaining natural language arguments with background knowledge. In , PROFILES-SEMEX 2019 : Joint Proceedings of the 6th International Workshop on Dataset PROFlLing and Search & the 1st Workshop on Semantic Explainability co-loc. with 18th International Semantic Web Conference (ISWC 2019) Auckland, NZ, Oct. 27, 2019 (S. 62–77). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Kobbe, J., Opitz, J., Becker, M., Hulpus, I., Stuckenschmidt, H. and Frank, A. (2019). Exploiting background knowledge for argumentative relation classification. In , 2nd Conference on Language, Data and Knowledge (LDK 2019) (S. 8:1–8:14). OASIcs – OpenAccess Series in Informatics, Leibniz-Zentrum für Informatik: Wadern.