Topic Ontologies for Arguments

authored by

Yamen Ajjour, Benno Stein, Johannes Kiesel, Martin Potthast

Abstract

Many computational argumentation tasks, such as stance classification, are topic-dependent: The effectiveness of approaches to these tasks depends largely on whether they are trained with arguments on the same topics as those on which they are tested. The key question is: What are these training topics? To answer this question, we take the first step of mapping the argumentation landscape with The Argument Ontology (TAO). TAO draws on three authoritative sources for argument topics: the World Economic Forum, Wikipedia’s list of controversial topics, and Debatepedia. By comparing the topics in our ontology with those in 59 argument corpora, we perform the first comprehensive assessment of their topic coverage. While TAO already covers most of the corpus topics, the corpus topics barely cover all the topics in TAO. This points to a new goal for corpus construction to achieve a broad topic coverage and thus better generalizability of computational argumentation approaches.

Organisation(s)

Natural Language Processing Section

External Organisation(s)

Bauhaus-Universität Weimar
Leipzig University

Type

Conference contribution

Pages

1381-1397

No. of pages

17

Publication date

2023

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computational Theory and Mathematics, Software, Linguistics and Language