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A bridge too far for artificial intelligence?: Automatic classification of stanzas in Spanish poetry

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dc.contributor.authorPérez Pozo, Álvaro
dc.contributor.authorRosa, Javier de la
dc.contributor.authorRos Muñoz, Salvador
dc.contributor.authorGonzález Blanco, Elena
dc.contributor.authorHernández Lorenzo, Laura
dc.contributor.authorSisto, Mirella de
dc.date.accessioned2024-09-02T09:47:37Z
dc.date.available2024-09-02T09:47:37Z
dc.date.issued2022
dc.descriptionThe registered version of this article, first published in "Journal of the Association for Information Science and Technology, 73(2), 258-267", is available online at the publisher's website: Wiley, https://doi.org/10.1002/asi.24532
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en "Journal of the Association for Information Science and Technology, 73(2), 258-267", está disponible en línea en el sitio web del editor: Wiley, https://doi.org/10.1002/asi.24532
dc.descriptionFunding information: H2020 European Research Council, Grant/Award Number: ERC-2015-STG-679528
dc.description.abstractThe rise in artificial intelligence and natural language processing techniques has increased considerably in the last few decades. Historically, the focus has been primarily on texts expressed in prose form, leaving mostly aside figurative or poetic expressions of language due to their rich semantics and syntactic complexity. The creation and analysis of poetry have been commonly carried out by hand, with a few computer-assisted approaches. In the Spanish context, the promise of machine learning is starting to pan out in specific tasks such as metrical annotation and syllabification. However, there is a task that remains unexplored and underdeveloped: stanza classification. This classification of the inner structures of verses in which a poem is built upon is an especially relevant task for poetry studies since it complements the structural information of a poem. In this work, we analyzed different computational approaches to stanza classification in the Spanish poetic tradition. These approaches show that this task continues to be hard for computers systems, both based on classical machine learning approaches as well as statistical language models and cannot compete with traditional computational paradigms based on the knowledge of experts.en
dc.description.versionversión publicada
dc.identifier.citationPérez Pozo, Á., de la Rosa, J., Ros, S., González-Blanco, E., Hernández, L., & de Sisto, M. (2022). A bridge too far for artificial intelligence?: Automatic classification of stanzas in Spanish poetry. Journal of the Association for Information Science and Technology, 73(2), 258-267. https://doi.org/10.1002/ASI.24532
dc.identifier.doihttps://doi.org/10.1002/asi.24532
dc.identifier.issn2330-1643
dc.identifier.urihttps://hdl.handle.net/20.500.14468/23593
dc.journal.issue2
dc.journal.titleJournal of the Association for Information Science and Technology
dc.journal.volume73
dc.language.isoen
dc.page.final267
dc.page.initial258
dc.publisherWiley
dc.relation.centerFacultad de Filología
dc.relation.departmentLiteratura Española y Teoría de la Literatura
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject62 Ciencias de las Artes y las Letras::6202 Teoría, análisis y crítica literarias
dc.titleA bridge too far for artificial intelligence?: Automatic classification of stanzas in Spanish poetryen
dc.typejournal articleen
dspace.entity.typePublication
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relation.isAuthorOfPublicationd25ad74f-42fc-47ac-911d-1e5515319a58
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relation.isAuthorOfPublication.latestForDiscoverybe1bfc00-6641-4ae9-91be-440c0945f461
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