A benchmark of Spanish language datasets for computationally driven research

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/120141
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Title: A benchmark of Spanish language datasets for computationally driven research
Authors: Candela, Gustavo | Sáez Fernández, María Dolores | Escobar Esteban, María Pilar | Marco Such, Manuel
Research Group/s: Lucentia | Transducens
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Collections as data | Data quality metrics | Digital libraries | GLAM labs
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: 13-Dec-2021
Publisher: SAGE Publications
Citation: Journal of Information Science. 2021. https://doi.org/10.1177/01655515211060530
Abstract: In the domain of Galleries, Libraries, Archives and Museums (GLAM) institutions, creative and innovative tools and methodologies for content delivery and user engagement have recently gained international attention. New methods have been proposed to publish digital collections as datasets amenable to computational use. Standardised benchmarks can be useful to broaden the scope of machine-actionable collections and to promote cultural and linguistic diversity. In this article, we propose a methodology to select datasets for computationally driven research applied to Spanish text corpora. This work seeks to encourage Spanish and Latin American institutions to publish machine-actionable collections based on best practices and avoiding common mistakes.
Sponsor: This research has been funded by the AETHER-UA (PID2020-112540RB-C43) Project from the Spanish Ministry of Science and Innovation.
URI: http://hdl.handle.net/10045/120141
ISSN: 0165-5515 (Print) | 1741-6485 (Online)
DOI: 10.1177/01655515211060530
Language: eng
Type: info:eu-repo/semantics/article
Rights: © The Author(s) 2021
Peer Review: si
Publisher version: https://doi.org/10.1177/01655515211060530
Appears in Collections:INV - TRANSDUCENS - Artículos de Revistas
INV - LUCENTIA - Artículos de Revistas

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