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神经符号论证挖掘:支持深度学习与推理的一个论据。

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

作者信息

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

机构信息

Department of Computer Science and Engineering, University of Bologna, Bologna, Italy.

Computer Science Department and Centre for Cognitive Science, TU Darmstadt, Darmstadt, Germany.

出版信息

Front Big Data. 2020 Jan 22;2:52. doi: 10.3389/fdata.2019.00052. eCollection 2019.

Abstract

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.

摘要

深度学习正在为论证挖掘领域做出卓越贡献,但现有方法在执行高级推理任务方面仍需填补差距。在本立场文件中,我们认为神经符号和统计关系学习在整合符号方法和亚符号方法以实现这一目标方面可以发挥关键作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7a8/7931943/1079442e4767/fdata-02-00052-g0001.jpg

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