The paper presents supervised methods for detecting and categorizing argument aspects. The approach is useful for computational analysis of argumentative structures in large collections of online discourse.
Citation: Ruckdeschel, M., & Wiedemann, G. (2022). Boundary Detection and Categorization of Argument Aspects via Supervised Learning. Proceedings of the 9th Workshop on Argument Mining, 126–136.
Publication: https://aclanthology.org/2022.argmining-1.12/