Mostra el registre parcial de l'element
dc.contributor.author | Accuosto, Pablo |
dc.contributor.author | Saggion, Horacio |
dc.date.accessioned | 2019-07-02T10:58:49Z |
dc.date.available | 2019-07-02T10:58:49Z |
dc.date.issued | 2019 |
dc.identifier.citation | 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 |
dc.identifier.isbn | 978-3-030-23280-1 |
dc.identifier.uri | http://hdl.handle.net/10230/41907 |
dc.description | 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. |
dc.description.abstract | 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. |
dc.description.sponsorship | This work is (partly) supported by the Spanish Government under the María de Maeztu Units of Excellence Programme (MDM-2015-0502). |
dc.format.mimetype | application/pdf |
dc.language.iso | eng |
dc.publisher | Springer |
dc.relation.ispartof | 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). |
dc.rights | © Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-23281-8_15 |
dc.title | Discourse-driven argument mining in scientific abstracts |
dc.type | info:eu-repo/semantics/conferenceObject |
dc.identifier.doi | http://dx.doi.org/10.1007/978-3-030-23281-8_15 |
dc.subject.keyword | Argument mining |
dc.subject.keyword | RST |
dc.subject.keyword | Scientific corpus |
dc.rights.accessRights | info:eu-repo/semantics/openAccess |
dc.type.version | info:eu-repo/semantics/acceptedVersion |