Argumentative evidences classification and argument scheme detection using tree kernels

Reference : Argumentative evidences classification and argument scheme detection using tree kernels


	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/52145

Title :	Argumentative evidences classification and argument scheme detection using tree kernels
Language :	English
Author, co-author :	Liga, Davide [University of Luxembourg > Faculty of Science, Technology and Medecine (FSTM) > > ; University of Bologna > CIRSFID]
Publication date :	2019
Main document title :	Proceedings of the 6th Workshop on Argument Mining
Pages :	92-97
Peer reviewed :	Yes
Event name :	ArgMining (hosted by ACL)
Event date :	01-08-2019
Event place (city) :	Florence
Keywords :	[en] Argument Mining ; NLP ; Argument schemes
Abstract :	[en] The purpose of this study is to deploy a novel methodology for classifying different argumentative support (supporting evidences) in arguments, without considering the context. The proposed methodology is based on the idea that the use of Tree Kernel algorithms can be a good way to discriminate between different types of argumentative stances without the need of highly engineered features. This can be useful in different Argumentation Mining sub-tasks. This work provides an example of classifier built using a Tree Kernel method, which can discriminate between different kinds of argumentative support with a high accuracy. The ability to distinguish different kinds of support is, in fact, a key step toward Argument Scheme classification.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/52145
DOI :	10.18653/v1/W19-4511
Commentary :	Proceedings of the 6th Workshop on Argument Mining

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