article

From Argument Diagrams to Argumentation Mining in Texts: A Survey

Authors:

Andreas Peldszus,

Manfred StedeAuthors Info & Claims

International Journal of Cognitive Informatics and Natural Intelligence, Volume 7, Issue 1

Pages 1 - 31

https://doi.org/10.4018/jcini.2013010101

Published: 01 January 2013 Publication History

Abstract

In this paper, the authors consider argument mining as the task of building a formal representation for an argumentative piece of text. Their goal is to provide a critical survey of the literature on both the resulting representations i.e., argument diagramming techniques and on the various aspects of the automatic analysis process. For representation, the authors also provide a synthesized proposal of a scheme that combines advantages from several of the earlier approaches; in addition, the authors discuss the relationship between representing argument structure and the rhetorical structure of texts in the sense of Mann and Thompsons 1988 RST. Then, for the argument mining problem, the authors also cover the literature on closely-related tasks that have been tackled in Computational Linguistics, because they think that these can contribute to more powerful argument mining systems than the first prototypes that were built in recent years. The paper concludes with the authors' suggestions for the major challenges that should be addressed in the field of argument mining.

References

[1]

Adachi-Bähr, S. 2006. Kontrastive Analyse von Gliederungsprinzipien in argumentativen schriftlichen Texten im Deutschen und Japanischen, volume 20, Ausgabe 1 of Arbeitspapiere und Materialien zur deutschen Sprache. Institut für Deutsche Sprache, Mannheim.

[2]

Apothéloz, D., Brandt, P.-Y., & Quiroz, G. 1993. The function of negation in argumentation. Journal of Pragmatics, 191, 23-38.

[3]

Asher, N., & Lascarides, A. 2003. Logics of conversation. Cambridge, UK: Cambridge University Press.

[4]

Azar, M. 1999. Argumentative text as rhetorical structure: An application of rhetorical structure theory. Argumentation, 13, 97-114.

[5]

Bal, B. K., & Saint-Dizier, P. 2009. Towards an analysis of argumentation structure and the strength of arguments in news editorials. In Proceedings of the AISB Symposium on Persuasive Technologies, Edinburgh, UK.

[6]

Beardsley, M. C. 1950. Practical logic. New York, NY: Prentice-Hall.

[7]

Blanco, E., Castell, N., & Moldovan, D. 2008. Causal relation extraction. In Proceedings of the Sixth International Conference on Language Resources and Evaluation LREC'08, Marrakech, Morocco pp. 310-313.

[8]

Carlson, L., Marcu, D., & Okurowski, M. E. 2003. 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. Dordrecht, Germany: Kluwer.

[9]

Dung, P. M. 1995. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence, 772, 321-357.

Digital Library

[10]

Feng, V. W., & Hirst, G. 2011. Classifying arguments by scheme. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR pp. 987-996.

[11]

Freeman, J. B. 1991. Dialectics and the macrostructure of argument. Berlin, Germany: Foris.

[12]

Freeman, J. B. 2011. Argument structure: Representation and theory. Argumentation Library 18. Springer.

[13]

Girju, R. 2003. Automatic detection of causal relations for question answering. In Proceedings of the ACL Workshop on Multilingual Summarization and Question Answering pp. 76-83.

[14]

Gordon, T. F. 2010. An overview of the carneades argumentation support system. In Reed, C. Ed., Dialectics, dialogue and argumentation. An examination of Douglas Walton's theories of reasoning and argument pp. 145-156. London, UK: King's College London.

[15]

Govier, T. 1985. A practical study of argument. Belmont, CA: Wadsworth.

[16]

Green, N. L. 2010. Representation of argumentation in text with rhetorical structure theory. Argumentation, 24, 181-196.

[17]

Grewendorf, G. 1980. Argumentation in der Sprachwissenschaft. Zeitschrift für Literaturwissenschaft und Linguistik, 1038/39, 129-150.

[18]

Hernault, H., Prendinger, H., duVerle, D., & Ishizuka, M. 2010. HILDA: A discourse parser using support vector machine classification. Dialogue and Discourse, 13, 1-33.

[19]

Khoo, C. S., Chan, S., & Niu, Y. 2000. Extracting causal knowledge from a medical database using graphical patterns. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics ACL.

Digital Library

[20]

Kienpointner, M. 1983. Argumentationsanalyse, volume 56 of Innsbrucker Beiträge zur Kulturwissenschaft. Innsbruck.

[21]

Kim, J.-J., & Park, J. C. 2006. Extracting contrastive information from biomedical literature. ACM Transactions on Asian Language Information Processing, 51, 44-60.

Digital Library

[22]

Klein, W. 1980. Argumentation und argument. Zeitschrift für Literaturwissenschaft und Linguistik, 1038/39, 9-56.

[23]

Knott, A. 1996. A data-driven methodology for motivating a set of coherence relations. PhD. thesis, University of Edinburgh.

[24]

Knott, A., & Sanders, T. 1998. The classification of coherence relations and their linguistic markers: An exploration of two languages. Journal of Pragmatics, 30, 135-175.

[25]

Kopperschmidt, J. 1989. Methodik der Argumentationsanalyse. Stuttgart: Frommann-Holzboog.

[26]

Mann, W., & Thompson, S. 1988. Rhetorical structure theory: Towards a functional theory of text organization. Text, 8, 243-281.

[27]

Marcu, D. 1999. A decision-based approach to rhetorical parsing. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, College Park, MD pp. 365-372.

[28]

Marcu, D. 2000. The theory and practice of discourse parsing and summarization. Cambridge, MA: MIT Press.

[29]

Mochales Palau, R., & Moens, M.-F. 2009. Argumentation mining: The detection, classification and structure of arguments in text. In Proceedings of the ICAIL 2009, Barcelona, Spain pp. 98-109.

[30]

Mulkar-Mehta, R., Welty, C. A., Hobbs, J. R., & Hovy, E. H. 2011. Using part-of relations for discovering causality. In Proceedings of FLAIRS Conference.

[31]

Öhlschläger, G. 1979. Linguistische Überlegungen zu einer Theorie der Argumentation. Niemeyer, Tübingen. Palau, R. M., & Moens, M.-F. 2011. Argumentation mining. Artificial Intelligence and Law, 191, 15-22.

[32]

Polanyi, L., & Scha, R. 1984. A syntactic approach to discourse semantics. In Proceedings of the 10th International Conference on Computational Linguistics and 22nd Annual Meeting of the Association for Computational Linguistics {ACL.}. Proceedings of COLING, 84, 413-419.

[33]

Pollock, J. L. 1995. Cognitive carpentry: A blueprint for how to build a person. Cambridge, MA: MIT Press.

[34]

Rahwan, I., & Reed, C. 2009. The argument interchange format. In Rahwan, I., & Simari, G. Eds., Argumentation in artificial intelligence pp. 383-402. New York, NY: Springer.

[35]

Reed, C., Walton, D., & Macagno, F. 2007. Argument diagramming in logic, law and artificial intelligence. The Knowledge Engineering Review, 221, 87-109.

Digital Library

[36]

Rooney, N., Wang, H., & Browne, F. 2012. Applying kernel methods to argumentation mining. In Proceedings of the 25th FLAIRS Conference.

[37]

Saint-Dizier, P. 2012. Processing natural language arguments with the TextCoop platform. Journal of Argumentation and Computation, 31, 49-82.

[38]

Stede, M. 2004. The potsdam commentary corpus. In Proceedings of the ACL Workshop on Discourse Annotation, Barcelona, Spain pp. 96-102.

[39]

Stede, M. 2011. Discourse processing. Morgan and Claypool.

[40]

Subba, R., & Di Eugenio, B. 2009. An effective discourse parser that uses rich linguistic information. In Proceedings of Human Language Technologies - The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics NAACL '09 pp. 566-574. Association for Computational Linguistics.

[41]

Taboada, M., & Mann, W. 2006. Applications of rhetorical structure theory. Discourse Studies, 84, 567-588.

[42]

Teufel, S., & Moens, M. 2002. Summarizing scientific articles - experiments with relevance and rhetorical status. Computational Linguistics, 284, 409-445.

Digital Library

[43]

Thomas, S. N. 1974. Practical reasoning in natural language. New York, NY: Prentice-Hall.

[44]

Toulmin, S. 1958. The uses of argument. Cambridge, UK: Cambridge University Press.

[45]

van Eemeren, F. H., & Grootendorst, R. 2004. A systematic theory of argumentation. The pragma-dialectic approach. Cambridge University Press.

[46]

Villalba, M. G., & Saint-Dizier, P. 2012. Some facets of argument mining for opinion analysis. In Proceedings of the 4th International Conference on Computational Models of Argument, Vienna, Switzerland COMMA 2012. IOS Publishing.

[47]

Walton, D. 2011. How to refute an argument using artificial intelligence. Studies in Logic . Grammar and Rhetoric, 2336, 123-154.

[48]

Walton, D., Reed, C., & Macagno, F. 2008. Argumentation schemes. Cambridge University Press.

[49]

Webber, B., Stone, M., Joshi, A., & Knott, A. 2003. Anaphora and discourse structure. Computational Linguistics, 294, 545-587.

Digital Library

[50]

Wunderlich, D. 1980. Pro und Kontra. Zeitschrift für Literaturwissenschaft und Linguistik, 1038/39, 109-127.

[51]

Wyner, A., Schneider, J., Atkinson, K., & Bench-Capon, T. 2012. Semi-automated argumentative analysis of online product reviews. In Proceedings of the 4th International Conference on Computational Models of Argument, Vienna, Switzerland COMMA 2012. IOS Publishing.

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cover image International Journal of Cognitive Informatics and Natural Intelligence

International Journal of Cognitive Informatics and Natural Intelligence Volume 7, Issue 1

January 2013

122 pages

ISSN:1557-3958

EISSN:1557-3966

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IGI Global

United States

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Published: 01 January 2013

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https://dl.acm.org/doi/10.1007/s10458-025-09692-x
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