Learning Argument Acceptability from Abstract Argumentation Frameworks

  • Hiroyuki KidoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10091)


This paper introduces argument-based decision-tree for learning acceptability of arguments. We specifically examine an attack relation existing between arguments, without referring to any contents, either sentences or words, existing in individual arguments. This idea is formalized using decision trees in which their attributes are instantiated by complete, preferred, stable and grounded extensions, respectively, defined by acceptability semantics. This study extracted 38 arguments and 4 utterers from an argument about euthanasia that actually took place on a social media site. Also, 21 training data were collected by asking them to express their attitudes either for or against the individual 38 arguments. By stratifying audiences in accordance with consistency with utterers, leave-two-out cross validation yielded results with a 0.73 AUC value, on average. This fact demonstrates that our argument-based decision-tree learning is expected to be fairly useful for agents who have a definite position on an issue of argument.


Acceptability learning Decision trees Argumentation 



This work was supported by JSPS KAKENHI Grant Number 15KT0041. We would like to thank the manager of SYNCLON for the active participation in this work and valuable comments and suggestions.


  1. 1.
    Dung, P.M.: On the acceptability of arguments and its funedamental role in nonmonotonic reasoning, logic programming, and \(n\)-person games. Artif. Intell. 77, 321–357 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web, pp. 519–528 (2003)Google Scholar
  3. 3.
    Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424 (2002)Google Scholar
  4. 4.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86 (2002)Google Scholar
  5. 5.
    Kim, S.-M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of the 20th International Conference on Computational Linguistics (2004)Google Scholar
  6. 6.
    Wiebe, J., Riloff, E.: Creating subjective and objective sentence classifiers from unannotated texts. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 486–497. Springer, Heidelberg (2005). doi: 10.1007/978-3-540-30586-6_53 CrossRefGoogle Scholar
  7. 7.
    Wilson, T., Wiebe, J., Hwa, R.: Just how mad are you? finding strong and weak opinion clauses. In: Proceedings of the 19th National Conference on Artifical Intelligence, pp. 761–767 (2004)Google Scholar
  8. 8.
    Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th International Conference on World Wide Web, pp. 342–351 (2005)Google Scholar
  9. 9.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177 (2004)Google Scholar
  10. 10.
    Popescu, A.-M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 339–346 (2005)Google Scholar
  11. 11.
    Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the International Conference on Web Search and Data Mining, pp. 231–240 (2008)Google Scholar
  12. 12.
    Synclon\(^{3}_{\beta }\).
  13. 13.
    Besnard, P., Doutre, S.: Checking the acceptability of a set of arguments. In: Proceedings of the 10th International Workshop on Nonmonotonic Reasoning (2004)Google Scholar
  14. 14.
    Egly, U., Gaggl, S.A., Woltran, S.: ASPARTIX: implementing argumentation frameworks using answer-set programming. In: Garcia de la Banda, M., Pontelli, E. (eds.) ICLP 2008. LNCS, vol. 5366, pp. 734–738. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-89982-2_67 CrossRefGoogle Scholar
  15. 15.
    Holmes, G., Donkin, A., Witten, I.H.: Weka: a machine learning workbench. In: Proceedings of the Second Australian and New Zealand Conference on Intelligent Information Systems, pp. 357–361 (1994)Google Scholar
  16. 16.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco (1993)Google Scholar
  17. 17.
    Koutra, D., Ke, T.-Y., Kang, U., Chau, D.H.P., Pao, H.-K.K., Faloutsos, C.: Unifying guilt-by-association approaches: theorems and fast algorithms. In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 245–260 (2011)Google Scholar
  18. 18.
    Amgoud, L., Serrurier, M.: Agents that argue and explain classifications. Auton. Agents Multi-Agent Syst. 16(2), 187–209 (2008)CrossRefGoogle Scholar
  19. 19.
    Palau, R.M., Moens, M.-F.: Argumentation mining: the detection, classification and structure of arguments in text. In: Proceedings of the 12th International Conference on Artificial Intelligence and Law, pp. 98–107 (2009)Google Scholar
  20. 20.
    Reed, C., Rowe, G.: Araucaria: software for argument analysis, diagramming and representation. Int. J. Artif. Intell. Tools 13(4), 961–979 (2004)CrossRefGoogle Scholar
  21. 21.
    Moina, M., Abkar, J., Bratko, I.: Argument based machine learning. Artif. Intell. 171(10–15), 922–937 (2007)MathSciNetGoogle Scholar
  22. 22.
    Kido, H., Ohsawa, Y.: Defensibility-based classification for argument mining. In: Proceedings of the 4th IEEE International Workshop on Data Mining in Networks, pp. 575–580 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.The University of TokyoBunkyo-kuJapan

Personalised recommendations