Discourse-driven argument mining in scientific abstracts
Discourse-driven argument mining in scientific abstracts
Citació
- Accuosto P, Saggion H. Discourse-driven argument mining in scientific abstracts. In: Métais E, Meziane F, Vadera S, Sugumaran V, Saraee M, editors. Natural Language Processing and Information Systems. 24th International Conference on Applications of Natural Language to Information Systems; 2019 Jun 26-28; Salford, UK. Heidelberg: Springer; 2019. p. 182-94. (LNCS, no. 11608). DOI: 10.1007/978-3-030-23281-8_15
Enllaç permanent
Descripció
Resum
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.Descripció
Comunicació presentada a: 24th International Conference on Applications of Natural Language to Information Systems (NLDB), celebrat del 26 al 28 de juny de 2019 a Salford, Regne Unit.