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.
Galassi, A., Kersting, K., Lippi, M., Shao, X., Torroni, P. (2020). Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning. FRONTIERS IN BIG DATA, 2, 1-6 [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.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.