Argument Mining: the Bottleneck of Knowledge and Language Resources

Patrick Saint-Dizier


Abstract
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 knowledge driven.
Anthology ID:
L16-1156
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
983–990
Language:
URL:
https://aclanthology.org/L16-1156
DOI:
Bibkey:
Cite (ACL):
Patrick Saint-Dizier. 2016. Argument Mining: the Bottleneck of Knowledge and Language Resources. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 983–990, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Argument Mining: the Bottleneck of Knowledge and Language Resources (Saint-Dizier, LREC 2016)
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PDF:
https://aclanthology.org/L16-1156.pdf