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.

Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning / Galassi, Andrea; Kersting, Kristian; Lippi, Marco; Shao, Xiaoting; Torroni, Paolo. - In: FRONTIERS IN BIG DATA. - ISSN 2624-909X. - ELETTRONICO. - 2:(2020), pp. 52.1-52.6. [10.3389/fdata.2019.00052]

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

Galassi, Andrea
;
Lippi, Marco;Torroni, Paolo
2020

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.
2020
Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning / Galassi, Andrea; Kersting, Kristian; Lippi, Marco; Shao, Xiaoting; Torroni, Paolo. - In: FRONTIERS IN BIG DATA. - ISSN 2624-909X. - ELETTRONICO. - 2:(2020), pp. 52.1-52.6. [10.3389/fdata.2019.00052]
Galassi, Andrea; Kersting, Kristian; Lippi, Marco; Shao, Xiaoting; Torroni, Paolo
File in questo prodotto:
File Dimensione Formato  
FINAL PUBLICATION.pdf

accesso aperto

Descrizione: Published paper
Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 394.97 kB
Formato Adobe PDF
394.97 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/716351
Citazioni
  • ???jsp.display-item.citation.pmc??? 1
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 6
social impact