Discourse-Driven Argument Mining in Scientific Abstracts

Accuosto P, Saggion H. Discourse-Driven Argument Mining in Scientific Abstracts. Natural Language Processing and Information Systems. NLDB 2019

Argument mining consists in the automatic identification of argumentative structures in texts. In this work we address the open question of whether discourse-level annotations can contribute to facilitate the identification of argumentative components and relations in scientific literature. We conduct a pilot study by enriching a corpus of computational linguistics abstracts that contains discourse annotations with a new argumentative annotation level. The results obtained from preliminary experiments confirm the potential value of the proposed approach.


Open access version at UPF e-repository: http://hdl.handle.net/10230/41907