Reference : Classifying argumentative stances of opposition 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/10993/52143

Title :	Classifying argumentative stances of opposition using Tree Kernels
Language :	English
Author, co-author :	Liga, Davide [University of Luxembourg > Faculty of Science, Technology and Medecine (FSTM) >]
	Palmirani, Monica [> >]
Publication date :	2019
Main document title :	ACAI 2019: Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence
Peer reviewed :	Yes
Event name :	International Conference on Algorithms, Computing and Artificial Intelligence (ACAI 2019)
Event date :	From 20-12-2019 to 22-12-2019
Keywords :	[en] Argument schemes ; Tree kernels ; Argument mining ; Natural Language Understanding
Abstract :	[en] The approach proposed in this study aims to classify argumentative oppositions. A major assumption of this work is that discriminating among different argumentative stances of support and opposition can facilitate the detection of Argument Schemes. While using Tree Kernels for classification problems can be useful in many Argument Mining sub-tasks, this work focuses on the classification of opposition stances. We show that Tree Kernels can be successfully used (alone or in combination with traditional textual vectorizations) to discriminate between different stances of opposition without requiring highly engineered features. Moreover, this study compare the results of Tree Kernels classifiers with the results of classifiers which use traditional features such as TFIDF and n-grams. This comparison shows that Tree Kernel classifiers can outperform TFIDF and n-grams classifiers.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/52143
DOI :	10.1145/3377713.3377717
Commentary :	2nd International Conference on Algorithms, Computing and Artificial Intelligence

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