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From Argument Diagrams to Argumentation Mining in Texts: A Survey

From Argument Diagrams to Argumentation Mining in Texts: A Survey

Andreas Peldszus, Manfred Stede
Copyright: © 2013 |Volume: 7 |Issue: 1 |Pages: 31
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781466631090|DOI: 10.4018/jcini.2013010101
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MLA

Peldszus, Andreas, and Manfred Stede. "From Argument Diagrams to Argumentation Mining in Texts: A Survey." IJCINI vol.7, no.1 2013: pp.1-31. http://doi.org/10.4018/jcini.2013010101

APA

Peldszus, A. & Stede, M. (2013). From Argument Diagrams to Argumentation Mining in Texts: A Survey. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 7(1), 1-31. http://doi.org/10.4018/jcini.2013010101

Chicago

Peldszus, Andreas, and Manfred Stede. "From Argument Diagrams to Argumentation Mining in Texts: A Survey," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 7, no.1: 1-31. http://doi.org/10.4018/jcini.2013010101

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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.

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