Argumentation mining in scientific literature: from computational linguistics to biomedicine

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• dc.contributor.author Accuosto, Pablo
• dc.contributor.author Neves, Mariana
• dc.contributor.author Saggion, Horacio
• dc.date.accessioned 2021-05-19T07:47:36Z
• dc.date.available 2021-05-19T07:47:36Z
• dc.date.issued 2021
• dc.description Comunicació presentada al BIR 2021: 11th International Workshop on Bibliometric-enhanced Information Retrieval, celebrat l'1 d'abril de 2021 de manera virtual.
• dc.description.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).en
• dc.description.sponsorship This work was (partly) supported by the Spanish Government under the María de Maeztu Units of Excellence Programme (MDM-2015-0502) and by the Research and Innovation Agency of Uruguay (ANII). We also acknowledge support from the project Context-aware Multilingual Text Simplification (ConMuTeS) PID2019-109066GB-I00/AEI/10.13039/501100011033 awarded by Ministerio de Ciencia, Innovación y Universidades (MCIU) and by Agencia Estatal de Investigación (AEI) of Spain.
• dc.format.mimetype application/pdf
• dc.identifier.citation Accuosto P, Neves M, Saggion H. Argumentation mining in scientific literature: from computational linguistics to biomedicine. In: Frommholz I, Mayr P, Cabanac G, Verberne S, editors. BIR 2021: 11th International Workshop on Bibliometric-enhanced Information Retrieval; 2021 Apr 1; Lucca, Italy. Aachen: CEUR; 2021. p. 20-36.
• dc.identifier.uri http://hdl.handle.net/10230/47600
• dc.language.iso eng
• dc.publisher CEUR Workshop Proceedings
• dc.relation.ispartof Frommholz I, Mayr P, Cabanac G, Verberne S, editors. BIR 2021: 11th International Workshop on Bibliometric-enhanced Information Retrieval; 2021 Apr 1; Lucca, Italy. Aachen: CEUR; 2021. p. 20-36
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-109066GB-I00
• dc.rights Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Argument miningen
• dc.subject.keyword Scientific corporaen
• dc.subject.keyword Domain adaptationen
• dc.subject.keyword Transformer modelsen
• dc.title Argumentation mining in scientific literature: from computational linguistics to biomedicineen
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion