Element 68Element 45Element 44Element 63Element 64Element 43Element 41Element 46Element 47Element 69Element 76Element 62Element 61Element 81Element 82Element 50Element 52Element 79Element 79Element 7Element 8Element 73Element 74Element 17Element 16Element 75Element 13Element 12Element 14Element 15Element 31Element 32Element 59Element 58Element 71Element 70Element 88Element 88Element 56Element 57Element 54Element 55Element 18Element 20Element 23Element 65Element 21Element 22iconsiconsElement 83iconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsElement 84iconsiconsElement 36Element 35Element 1Element 27Element 28Element 30Element 29Element 24Element 25Element 2Element 1Element 66

Boundary Detection and Categorization of Argument Aspects via Supervised Learning

Boundary Detection and Categorization of Argument Aspects via Supervised Learning

Mattes Ruckdeschel und Dr. Gregor Wiedemann beschreiben in diesem Konferenzpapier 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.

Zum Artikel (PDF)
 

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.


Ruckdeschel, M.; Wiedemann, G. (2022): Boundary Detection and Categorization of Argument Aspects via Supervised Learning. In: Proceedings of the 9th Workshop on Argument Mining, pages 126–136, Online and in Gyeongju, Republic of Korea. International Conference on Computational Linguistics. (pdf)

Boundary Detection and Categorization of Argument Aspects via Supervised Learning

Mattes Ruckdeschel und Dr. Gregor Wiedemann beschreiben in diesem Konferenzpapier 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.

Zum Artikel (PDF)
 

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.


Ruckdeschel, M.; Wiedemann, G. (2022): Boundary Detection and Categorization of Argument Aspects via Supervised Learning. In: Proceedings of the 9th Workshop on Argument Mining, pages 126–136, Online and in Gyeongju, Republic of Korea. International Conference on Computational Linguistics. (pdf)

Infos zur Publikation

Erscheinungsjahr

2022

ÄHNLICHE PUBLIKATIONEN UND VERWANDTE PROJEKTE

Newsletter

Infos über aktuelle Projekte, Veranstaltungen und Publikationen des Instituts.

NEWSLETTER ABONNIEREN!