Mattes Ruckdeschel und Dr. Gregor Wiedemann beschreiben in einem Konferenzpapier unter dem englischsprachigen Originaltitel „Boundary Detection and Categorization of Argument Aspects via Supervised Learning“ Analysemöglichkeiten zur automatischen Erkennung von Argument-Aspekten, mit denen pro/kontra-Argumente inhaltlich kategorisiert werden. Auf Basis der Arbeit können Argumentationsmuster und ihre Veränderungen in öffentlichen Diskursen, z.B. in Nachrichtentexten oder Tweets, detailliert automatisch untersuchen werden. Das peer-reviewed Paper wurde im Rahmen des 9. Workshops on Argument Mining präsentiert.
Abstract
Aspect-based argument mining (ABAM) is the task of automatic detection and categorization of argument aspects, i.e. the parts of an argumentative text that contain the issue-specific key rationale for its conclusion. From empirical data, overlapping but not congruent sets of aspect categories can be derived for different topics. So far, two supervised approaches to detect aspect boundaries, and a smaller number of unsupervised clustering approaches categorizing groups of similar aspects have been proposed. In this paper, we introduce the Argument Aspect Corpus (AAC) which contains token-level annotations of aspects in 3,547 argumentative sentences from three highly debated topics. This dataset enables both the supervised learning of boundaries and the categorization of argument aspects. During the design of our annotation process, we noticed that it is not clear from the outset at which contextual unit aspects should be coded. We, thus, experiment with classification at the token, chunk, and sentence level granularity. Our finding is that the chunk level provides the most useful information for applications. At the same time, it produces the best-performing results in our tested supervised learning setups.