Ioana Hulpu


2023

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Effect Graph: Effect Relation Extraction for Explanation Generation
Jonathan Kobbe | Ioana Hulpu | Heiner Stuckenschmidt
Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)

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.