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Knowledge-Driven Argument Mining: what we learn from corpus analysis

Saint-Dizier, Patrick Knowledge-Driven Argument Mining: what we learn from corpus analysis. (2016) In: 6th International Conference on Computational Models of Argument (COMMA 2016), 10 September 2016 - 12 September 2016 (Potsdam, Germany).

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Abstract

Given a controversial issue, argument mining from texts in natural language is extremely challenging: besides linguistic aspects, domain knowledge is often required together with appropriate forms of inferences to identify arguments. Via the the analysis of various corpora, this contribution explores the types of knowledge that are required to develop an efficient argument mining system.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to Universität Potsdam. The original PDF is available at: http://www.ling.uni-potsdam.de/comma2016/pdf/FLA16-proceedings.pdf
HAL Id:hal-01436201
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
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Deposited By: IRIT IRIT
Deposited On:06 Jan 2017 13:25

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