This paper describes an approach for supporting automatic satire detection through effective deep learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection problems. We both trained and tested the system exploiting articles derived from two important satiric blogs, Lercio and IlFattoQuotidiano, and significant Italian newspapers.

Towards a deep-learning-based methodology for supporting satire detection

Pilato G;Schicchi D
2021

Abstract

This paper describes an approach for supporting automatic satire detection through effective deep learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection problems. We both trained and tested the system exploiting articles derived from two important satiric blogs, Lercio and IlFattoQuotidiano, and significant Italian newspapers.
2021
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
Inglese
DMSVIVA 2021: 27th International DMS Conference on Visualization and Visual Languages
92
96
http://www.scopus.com/record/display.url?eid=2-s2.0-85112776121&origin=inward
Sì, ma tipo non specificato
29 June 2021 - 30 June 2021
Virtual, Pittsburgh
Visual languages
Deep learning
2
none
Cuzzocrea A.; Bosco G.L.; Maiorana M.; Pilato G.; Schicchi D.
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/429778
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