Classifying arguments by scheme

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 987–996, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Classifying Arguments by Scheme Vanessa Wei Feng Department of Computer Science University of Toronto Toronto, ON, M5S 3G4, Canada weifeng@cs.toronto.edu Graeme Hirst Department of Computer Science University of Toronto Toronto, ON, M5S 3G4, Canada gh@cs.toronto.edu Abstract Argumentation schemes are structures or tem- plates for various kinds of arguments. Given the text of an argument with premises and con- clusion identified, we classify it as an instance of one of five common schemes, using features specific to each scheme. We achieve accura- cies of 63–91% in one-against-others classifi- cation and 80–94% in pairwise classification (baseline = 50% in both cases). 1 Introduction We investigate a new task in the computational anal- ysis of arguments: the classification of arguments by the argumentation schemes that they use. An ar- gumentation scheme, informally, is a framework or structure for a (possibly defeasible) argument; we will give a more-formal definition and examples in Section 3. Our work is motivated by the need to de- termine the unstated (or implicitly stated) premises that arguments written in natural language normally draw on. Such premises are called enthymemes. For instance, the argument in Example 1 consists of one explicit premise (the first sentence) and a con- clusion (the second sentence): Example 1 [Premise:] The survival of the entire world is at stake. [Conclusion:] The treaties and covenants aiming for a world free of nuclear arsenals and other con- ventional and biological weapons of mass destruc- tion should be adhered to scrupulously by all na- tions. Another premise is left implicit — “Adhering to those treaties and covenants is a means of realizing survival of the entire world”. This proposition is an enthymeme of this argument. Our ultimate goal is to reconstruct the en- thymemes in an argument, because determining these unstated assumptions is an integral part of un- derstanding, supporting, or attacking an entire argu- ment. Hence reconstructing enthymemes is an im- portant problem in argument understanding. We be- lieve that first identifying the particular argumenta- tion scheme that an argument is using will help to bridge the gap between stated and unstated proposi- tions in the argument, because each argumentation scheme is a relatively fixed “template” for arguing. That is, given an argument, we first classify its ar- gumentation scheme; then we fit the stated proposi- tions into the corresponding template; and from this we infer the enthymemes. In this paper, we present an argument scheme classification system as a stage following argument detection and proposition classification. First in Sec- tion 2 and Section 3, we introduce the background to our work, including related work in this field, the two core concepts of argumentation schemes and scheme-sets, and the Araucaria dataset. In Section 4 and Section 5 we present our classification system, including the overall framework, data preprocessing, feature selection, and the experimental setups. In the remaining section, we present the essential ap- …

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