Classifying arguments by scheme

Citation: Vanessa Wei Feng and Graeme Hirst (2011) Classifying arguments by scheme. HLT (RSS)
Internet Archive Scholar (search for fulltext): Classifying arguments by scheme
Download: http://www.aclweb.org/anthology/P/P11/P11-1099.pdf
Tagged: Computer Science (RSS) machine learning (RSS), argumentation schemes (RSS), argumentation mining (RSS)

Summary

The goal is to automatically identify Walton's Argumentation schemes. The approach is machine learning, using Argument research corpus as a training set.

The authors work with 5 schemes and report "We achieve accuracies of 63–91% in one-against-others classification and 80–94% in pairwise classification (baseline of 50% in both cases)."

Schemes used

1. Argument from example
2. Argument from cause to effect
3. Practical reasoning
4. Argument from consequences
5. Argument from verbal classification

Selected References

• Walton, D.; Reed, C.; and Macagno, F. 2008. Argumentation schemes. Cambridge University Press.

• Robin Cohen. 1987. Analyzing the structure of argumentative discourse. Computational Linguistics, 13(1–2):11–24.

• Judith Dick. 1991a. A conceptual, case-relation representation of text for intelligent retrieval. Ph.D. thesis, Faculty of Library and Information Science, University of Toronto, April.

• Judith Dick. 1991b. Representation of legal text for conceptual retrieval. In Proceedings, Third International Conference on Artificial Intelligence and Law, pages 244–252, Oxford, June.

• Sarah George, Ingrid Zukerman, and Michael Niemann. 2007. Inferences, suppositions and explanatory extensions in argument interpretation. User Modeling and User-Adapted Interaction, 17(5):439–474.

Cited related work

Raquel Mochales' thesis work; the following are cited (others are covered elsewhere on Acawiki):

• Raquel Mochales and Marie-Francine Moens. 2008. Study on the structure of argumentation in case law. In Proceedings of the 2008 Conference on Legal Knowledge and Information Systems, pages 11–20, Amsterdam, The Netherlands. IOS Press.

• Raquel Mochales and Marie-Francine Moens. 2009a. Argumentation mining: The detection, classification and structure of arguments in text. In ICAIL ’09: Proceedings of the 12th International Conference on Arti- ficial Intelligence and Law, pages 98–107, New York, NY, USA. ACM.

• Raquel Mochales and Marie-Francine Moens. 2009b. Automatic argumentation detection and its role in law and the semantic web. In Proceedings of the 2009 Conference on Law, Ontologies and the Semantic Web, pages 115–129, Amsterdam, The Netherlands. IOS Press.

• Marie-Francine Moens, Erik Boiy, Raquel Mochales Palau, and Chris Reed. 2007. Automatic detection of arguments in legal texts. In ICAIL ’07: Proceedings of the 11th International Conference on Artificial Intelligence and Law, pages 225–230, New York, NY, USA. ACM.

Theoretical and Practical Relevance

In my view, this points to a need for a real gold standard argumentation corpus.

Mentioned by scholars

• ACL round-up

Extended version (Master's thesis)

Vanessa Wei Feng. 2010. Classifying arguments by scheme (MSc thesis). Technical report, Department of Computer Science, University of Toronto, November. http://ftp.cs.toronto.edu/pub/gh/Feng-MSc-2010.pdf