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.
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE) |
| Editors | Bhavana Dalvi Mishra, Greg Durrett, Peter Jansen, Danilo Neves Ribeiro, Jason Wei |
| Place of Publication | Toronto |
| Publisher | Association for Computational Linguistics |
| Pages | 116-127 |
| Number of pages | 12 |
| DOIs | |
| Publication status | Published - 13 Jun 2023 |