Reference : Detecting “slippery slope” and other argumentative stances of opposition using tree k...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
Detecting “slippery slope” and other argumentative stances of opposition using tree kernels in monologic discourse
Liga, Davide mailto [University of Luxembourg > Faculty of Science, Technology and Medecine (FSTM) >]
Palmirani, Monica [> >]
International Joint Conference on Rules and Reasoning
Springer, Cham
RuleML+RR 2019
From 16-09-2019 to 19-09-2019
[en] Argument Mining ; Tree kernels ; Argument schemes
[en] The aim of this study is to propose an innovative methodology to classify argumentative stances in a monologic argumentative context. Particularly, the proposed approach shows that Tree Kernels can be used in combination with traditional textual vectorization to discriminate between different stances of opposition without the need of extracting highly engineered features. This can be useful in many Argument Mining sub-tasks. In particular, this work explores the possibility of classifying opposition stances by training multiple classifiers to reach different degrees of granularity. Noticeably, discriminating support and opposition stances can be particularly useful when trying to detect Argument Schemes, one of the most challenging sub-task in the Argument Mining pipeline. In this sense, the approach can be also considered as an attempt to classify stances of opposition that are related to specific Argument Schemes.
Researchers ; Professionals ; Students
International Joint Conference on Rules and Reasoning

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