Classifying arguments by scheme

Vanessa Wei Feng and Graeme Hirst
Department of Computer Science, University of Toronto


Abstract

Argumentation schemes are structures or templates for various kinds of arguments. Given the text of an argument with premises and conclusion identified, we classify it as an instance of one of five common schemes, using features specific to each scheme. We achieve accuracies of 63–91% in one-against-others classification and 80–94% in pairwise classification (baseline = 50% in both cases).




Full paper: http://www.aclweb.org/anthology/P/P11/P11-1099.pdf