Building and Analyzing Corpus to Investigate Appropriateness of Argumentative Discourse Structure for Facilitating Consensus

  • Tatiana Zidrasco
  • Shun Shiramatsu
  • Jun Takasaki
  • Tadachika Ozono
  • Toramatsu Shintani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6097)


Clarifying characteristics of appropriate argumentative discourse is important for developing computer assisted argumentation systems. We describe the analysis of argumentative discourse structure on the basis of Rhetorical Structure Theory in order to clarify what kind of argumentative discourse structure should be considered appropriate. We think that there exist specific agreement-oriented sequences of rhetorical relations in argumentative discourse that tend to lead to an agreement. We build a small argumentative corpus annotated with rhetorical relations and calculate posteriori probability for rhetorical relations bigrams to investigate what rhetorical relations precede agreement.


corpus analysis argumentative discourse consensus building rhetorical structure theory 


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  1. 1.
    Carlson, L., Marcu, D., Okurowski, M.E.: Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory. In: van Kuppevelt, J., Smith, R. (eds.) Current Directions in Discourse and Dialogue, pp. 85–112. Kluwer Academic Publishers, Dordrecht (2003)CrossRefGoogle Scholar
  2. 2.
    Mann, W.C., Thompson, S.A.: Rhetorical Structure Theory: A Theory of Text Organization, Reprinted from the Structure of Discourse (1987)Google Scholar
  3. 3.
    Taboada, M., Mann, W.C.: Applications of Rhetorical Structure Theory. Discourse Studies 8(4), 567–588 (2005)CrossRefGoogle Scholar
  4. 4.
    Daradoumis, T.: Towards a Representation of the Rhetorical structure of Interrupted Exchanges. In: Trends in Natural Language Generation: An Artificial Intelligence Perspective, pp. 106–124. Springer, Berlin (1996)CrossRefGoogle Scholar
  5. 5.
    Stent, A.: Rhetorical structure in dialog. In: Proceedings of First International Conference on Natural Language Generation (INLG 2000), pp. 247–252. Mitzpe Ramon, Israel (2000)Google Scholar
  6. 6.
    Reed, C., Rowe, G.: Araucaria: Software for Argument Analysis, Diagramming and Representation (2004)Google Scholar
  7. 7.
    Reed, C., Grasso, F.: Recent Advances in Computational Models of Natural Argument. Wiley Periodicals, Inc. Int. J. Int. Syst. 22, 1–15 (2007)CrossRefzbMATHGoogle Scholar
  8. 8.
    Verheij, B.: Artificial Argument Assistants for Defeasible Argumentation. Elsevier B.V, Amsterdam (2001)Google Scholar
  9. 9.
    Moore, J.D., Paris, C.L.: Planning text for advisory dialogues: Capturing Intentional and Rhetorical Information. In: Computational linguistics - Association for Computational Linguistics, pp. 651–694. MIT Press, Cambridge (1993)Google Scholar
  10. 10.
    Jovanovic, N., den Akker, R.o., Nijholt, A.: A Corpus for Studying Addressing Behaviour in Multi-Party Dialogues. Springer Science + Business Media B.V, Heidelberg (2006)Google Scholar
  11. 11.
    Hashida, K.: Semantic Authoring and Semantic Computing. In: Sakurai, A., Hasida, K., Nitta, K. (eds.) JSAI 2003. LNCS (LNAI), vol. 3609, pp. 137–149. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Mochales, R., Ieven, A.: Creating an Argumentation Corpus: Do Theories Apply to Real Arguments? In: ICALL-2009, Barcelona, Spain (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tatiana Zidrasco
    • 1
    • 2
  • Shun Shiramatsu
    • 1
  • Jun Takasaki
    • 1
  • Tadachika Ozono
    • 1
  • Toramatsu Shintani
    • 1
  1. 1.Department of Computer Science and EngineeringNagoya Institute of TechnologyGikiso-cho, Showa-ku, Nagoya, AichiJapan
  2. 2.Applied Informatics DepartmentTechnical University of MoldovaChisinauMoldova

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