Effect graph: Effect relation extraction for explanation generation


Kobbe, Jonathan ; Hulpus, Ioana ; Stuckenschmidt, Heiner



URL: https://aclanthology.org/2023.nlrse-1.9
Document Type: Conference or workshop publication
Year of publication: 2023
Book title: Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)
Page range: 116-127
Conference title: 1st Workshop on Natural Language Reasoning and Structured Explanations
Location of the conference venue: Toronto, Canada
Date of the conference: 13.07.2023
Publisher: Dalvi Mishra, Bhavana ; Durett, Greg ; Jansen, Peter ; Ribeiro, Danilo Neves ; Wei, Jason
Place of publication: Toronto, Canada
Publishing house: Association for Computational Linguistics
Related URLs:
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
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.




Dieser Eintrag ist Teil der Universitätsbibliographie.




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