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19 A.I. & L. 1 (2011)

handle is hein.journals/artinl19 and id is 1 raw text is: Artif Intell Law (2011) 19:1-22 DOI 10.1007/s10506-010-9104-x Argumentation mining Raquel Mochales - Marie-Francine Moens Published online: 11 April 2011 © Springer Science+Business Media B.V. 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 under- standing 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 argu- mentation. 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 1 Introduction Argumentation can be defined as a process whereby arguments are constructed, exchanged and evaluated in light of their interactions with other arguments, each of which comprises a set of premises, pieces of evidence, offered in support of a claim. The claim is a proposition, an idea which is either true or false, put forward by somebody as true. The claim of an argument is normally called its conclusion. R. Mochales (E) - M.-F. Moens Katholieke Universiteit Leuven, Leuven, Belgium e-mail: raquel.mochales@cs.kuleuven.be M.-F. Moens e-mail: marie-francine.moens@cs.kuleuven.be I_ Springer

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