Argumentation mining in scientific literature: From computational linguistics to biomedicine

  • Authors
  • Accuosto P, Neves ML, Saggion H
  • UPF authors
  • SAGGION, HORACIO;
  • Type
  • Articles de recerca
  • Journal títle
  • CEUR Workshop Proceedings
  • Publication year
  • 2021
  • Volume
  • 2847
  • Pages
  • 20-36
  • ISSN
  • 1613-0073
  • Publication State
  • Publicat
  • Abstract
  • In this work we propose to tackle the limitations posed by the lack of annotated data for argument mining in scientific texts by annotating argumentative units and relations in research abstracts in two scientific domains. We evaluate our annotations by computing inter-annotator agreements, which range from moderate to substantial according to the difficulty level of the tasks and domains. We use our newly annotated corpus to fine-tune BERT-based models for argument mining in single and multi-task settings, finally exploring the adaptation of models trained in one scientific discipline (computational linguistics) to predict the argumentative structure of abstracts in a different one (biomedicine). © 2021 Copyright for this paper by its authors.
  • Complete citation
  • Accuosto P, Neves ML, Saggion H. Argumentation mining in scientific literature: From computational linguistics to biomedicine. CEUR Workshop Proceedings 2021; 2847( ): 20-36.
Bibliometric indicators
  • Índex Scimago de 0.177 (2020)