Abstract
Argumentation is an important means of communication. For describing especially arguments about consequences, the notion of effect relations has been introduced recently. We propose a method to extract effect relations from large text resources and apply it on encyclopedic and argumentative texts. By connecting the extracted relations, we generate a knowledge graph which we call effect graph. For evaluating the effect graph, we perform crowd and expert annotations and create a novel dataset. We demonstrate a possible use case of the effect graph by proposing a method for explaining arguments from consequences.- Anthology ID:
- 2023.nlrse-1.9
- Volume:
- Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)
- Month:
- June
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- NLRSE
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 116–127
- Language:
- URL:
- https://aclanthology.org/2023.nlrse-1.9
- DOI:
- Cite (ACL):
- Jonathan Kobbe, Ioana Hulpu, and Heiner Stuckenschmidt. 2023. Effect Graph: Effect Relation Extraction for Explanation Generation. In Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE), pages 116–127, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Effect Graph: Effect Relation Extraction for Explanation Generation (Kobbe et al., NLRSE 2023)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2023.nlrse-1.9.pdf