Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning

Andrea Galassi; Kristian Kersting; Marco Lippi; Xiaoting Shao; Paolo Torroni

doi:10.3389/fdata.2019.00052

Frontiers in Big Data (Jan 2020)

Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning

Andrea Galassi,
Kristian Kersting,
Marco Lippi,
Xiaoting Shao,
Paolo Torroni

Affiliations

Andrea Galassi: Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
Kristian Kersting: Computer Science Department and Centre for Cognitive Science, TU Darmstadt, Darmstadt, Germany
Marco Lippi: Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, Italy
Xiaoting Shao: Computer Science Department and Centre for Cognitive Science, TU Darmstadt, Darmstadt, Germany
Paolo Torroni: Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

DOI: https://doi.org/10.3389/fdata.2019.00052
Journal volume & issue: Vol. 2

Abstract

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Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal.

Published in Frontiers in Big Data

ISSN: 2624-909X (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Technology: Technology (General): Industrial engineering. Management engineering: Information technology
Website: https://www.frontiersin.org/journals/big-data

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