Argument Mining: The bottleneck of knowledge and language resources

Patrick Saint Dizier (1)

1 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage

Given a controversial issue, argument mining from natural language texts (news papers, and any form of text on the Internet) is extremely challenging: domain knowledge is often required together with appropriate forms of inferences to identify arguments. This contribution explores the types of knowledge that are required and how they can be paired with reasoning schemes, language processing and language resources to accurately mine arguments. We show via corpus analysis that the Generative Lexicon, enhanced in different manners and viewed as both a lexicon and a domain knowledge representation, is a relevant approach. In this paper, corpus annotation for argument mining is first developed, then we show how the generative lexicon approach must be adapted and how it can be paired with language processing patterns to extract and specify the nature of arguments. Our approach to argument mining is thus {\bf knowledge driven

Mots clés: Argument mining; Knowledge representation; Generative lexicon

Domaines: Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Logique en informatique [cs.LO]; Informatique et langage [cs.CL]

Patrick Saint Dizier. Argument Mining: The bottleneck of knowledge and language resources. 10th International Conference on Language Resources and Evaluation (LREC 2016), May 2016, Portoroz, Slovenia. pp. 983-990. ⟨hal-01436203⟩