Skip to Main content Skip to Navigation
Conference papers

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.
Complete list of metadata

Cited literature [14 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01436201
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Monday, January 16, 2017 - 11:39:33 AM
Last modification on : Thursday, March 18, 2021 - 2:24:48 PM
Long-term archiving on: : Monday, April 17, 2017 - 1:39:33 PM

File

saintdizier_17228.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01436201, version 1
  • OATAO : 17228

Citation

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⟩

Share

Metrics

Record views

137

Files downloads

145