Adpositional Argumentation (AdArg) is a new method for annotating argumentative discourse that represents linguistic and pragmatic information in argumentative adpositional trees. In this paper, we explain how the representation of claims and individual arguments provide the building blocks for more complex argumentation structures. We illustrate the abstract trees representing the systematic possibilities of a claim (one statement), minimal argument (one conclusion, one premise), convergent argumentation (one conclusion, multiple premises), as well as serial argumentation, when the same linguistic material plays the double role of the premise of a given argument and the conclusion of a subargument.

Complex Arguments in Adpositional Argumentation

Benini M.;
2021-01-01

Abstract

Adpositional Argumentation (AdArg) is a new method for annotating argumentative discourse that represents linguistic and pragmatic information in argumentative adpositional trees. In this paper, we explain how the representation of claims and individual arguments provide the building blocks for more complex argumentation structures. We illustrate the abstract trees representing the systematic possibilities of a claim (one statement), minimal argument (one conclusion, one premise), convergent argumentation (one conclusion, multiple premises), as well as serial argumentation, when the same linguistic material plays the double role of the premise of a given argument and the conclusion of a subargument.
2021
AI^3 2021 Advances in Argumentation in Artificial Intelligence Proceedings of the 5th Workshop on Advances in Argumentation in Artificial Intelligence 2021 co-located with the 20th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2021)
5th Workshop on Advances in Argumentation in Artificial Intelligence 2021
Milano
2021
File in questo prodotto:
File Dimensione Formato  
short4 (1).pdf

accesso aperto

Descrizione: Articolo principale
Tipologia: Versione Editoriale (PDF)
Licenza: Creative commons
Dimensione 223.46 kB
Formato Adobe PDF
223.46 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/11383/2133428
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact