Artificial Intelligence and Law 19 (1):1-22 (2011)

Abstract
Argumentation mining aims to automatically detect, classify and structure argumentation in text. Therefore, argumentation mining is an important part of a complete argumentation analyisis, i.e. understanding the content of serial arguments, their linguistic structure, the relationship between the preceding and following arguments, recognizing the underlying conceptual beliefs, and understanding within the comprehensive coherence of the specific topic. We present different methods to aid argumentation mining, starting with plain argumentation detection and moving forward to a more structural analysis of the detected argumentation. Different state-of-the-art techniques on machine learning and context free grammars are applied to solve the challenges of argumentation mining. We also highlight fundamental questions found during our research and analyse different issues for future research on argumentation mining
Keywords Argumentation  Information extraction  Text classification  Document structure
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DOI 10.1007/s10506-010-9104-x
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References found in this work BETA

The Uses of Argument.Stephen E. Toulmin - 1958 - Cambridge University Press.
The Uses of Argument.Stephen E. Toulmin - 1958 - Philosophy 34 (130):244-245.
Structural Proof Theory.Sara Negri, Jan von Plato & Aarne Ranta - 2001 - Cambridge University Press.

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Citations of this work BETA

A Means-End Classification of Argumentation Schemes.Fabrizio Macagno - 2015 - In Frans van Eemeren & Bart Garssen (eds.), Reflections on theoretical issues in argumentation theory. Cham, Switzerland: pp. 183-201.
Classifying the Patterns of Natural Arguments.Fabrizio Macagno & Douglas Walton - 2015 - Philosophy and Rhetoric 48 (1): 26-53.

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