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2020 | OriginalPaper | Buchkapitel

Classification of Relationship in Argumentation Using Graph Convolutional Network

verfasst von : Dimmy Magalhães, Aurora Pozo

Erschienen in: ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium

Verlag: Springer International Publishing

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Abstract

The Argument Relationship Prediction is one of the tasks of Argumentation Mining that aim to find connections between arguments (or parts thereof). This task is considered as one of the most complex stages of argumentation. Concomitant to that, the Graph Convolutional Network (GCN) has been successfully applied to graph-based applications. In this study, we join the relationship prediction challenge with the ability of GCN to classification. We propose ArgGCN, a framework based in GCN method applied to the classification of relationships between arguments. The arguments are considered as short texts, and we abstracted the recognition of unitary elements from them (such as claims and evidence). In this study, we achieved promising results on the UKP Aspect, AFS, and Microtext corpus.

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https://github.com/dimmykarson/argGCN.

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Titel: Classification of Relationship in Argumentation Using Graph Convolutional Network
verfasst von: Dimmy Magalhães
Aurora Pozo
Verlag: Springer International Publishing
Buch: ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium
Print ISBN: 978-3-030-55813-0

Electronic ISBN: 978-3-030-55814-7

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-55814-7_5

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