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

Patrick Saint Dizier 1
1 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage
IRIT - Institut de recherche en informatique de Toulouse
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.
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Submitted on : Monday, January 16, 2017 - 11:39:33 AM
Last modification on : Thursday, March 18, 2021 - 2:24:48 PM
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  • HAL Id : hal-01436201, version 1
  • OATAO : 17228


Patrick Saint Dizier. Knowledge-Driven Argument Mining: what we learn from corpus analysis. 6th International Conference on Computational Models of Argument (COMMA 2016), Sep 2016, Potsdam, Germany. pp. 65-72. ⟨hal-01436201⟩



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