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dc.contributor.authorGoenaga Azcarate, Iakes
dc.contributor.authorAndrés Santamaría, Edgar
dc.contributor.authorGojenola Galletebeitia, Koldobika
dc.contributor.authorAtutxa Salazar, Aitziber
dc.date.accessioned2023-12-20T14:25:17Z
dc.date.available2023-12-20T14:25:17Z
dc.date.issued2023-06
dc.identifier.citationBMC Bioinformatics 24 : (2023) // Article ID 265es_ES
dc.identifier.issn1471-2105
dc.identifier.urihttp://hdl.handle.net/10810/63451
dc.description.abstractBackground Unlike diseases, automatic recognition of disabilities has not received the same attention in the area of medical NLP. Progress in this direction is hampered by obstacles like the lack of annotated corpus. Neural architectures learn to translate sequences from spontaneous representations into their corresponding standard representations given a set of samples. The aim of this paper is to present the last advances in monolingual (Spanish) and crosslingual (from English to Spanish and vice versa) automatic disability annotation. The task consists of identifying disability mentions in medical texts written in Spanish within a collection of abstracts from journal papers related to the biomedical domain. Results In order to carry out the task, we have combined deep learning models that use different embedding granularities for sequence to sequence tagging with a simple acronym and abbreviation detection module to boost the coverage. Conclusions Our monolingual experiments demonstrate that a good combination of different word embedding representations provide better results than single representations, significantly outperforming the state of the art in disability annotation in Spanish. Additionally, we have experimented crosslingual transfer (zero-shot) for disability annotation between English and Spanish with interesting results that might help overcoming the data scarcity bottleneck, specially significant for the disabilities.es_ES
dc.description.sponsorshipThis work was partially funded by the Spanish Ministry of Science and Innovation (MCI/AEI/FEDER, UE, DOTT-HEALTH/PAT-MED PID2019-106942RB-C31), the Basque Government (IXA IT1570-22), MCIN/AEI/ 10.13039/501100011033 and European Union NextGeneration EU/PRTR (DeepR3, TED2021-130295B-C31) and the EU ERA-Net CHIST-ERA and the Spanish Research Agency (ANTIDOTE PCI2020-120717-2).es_ES
dc.language.isoenges_ES
dc.publisherBMCes_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/PID2019-106942RB-C31es_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/TED2021-130295B-C31es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.titleAdvances in monolingual and crosslingual automatic disability annotation in Spanishes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the mate- rial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publi cdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the dataes_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05372-3es_ES
dc.identifier.doi10.1186/s12859-023-05372-3
dc.departamentoesLenguajes y sistemas informáticoses_ES
dc.departamentoeuHizkuntza eta sistema informatikoakes_ES


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© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third
party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the mate-
rial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or
exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://
creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publi
cdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data
Except where otherwise noted, this item's license is described as © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the mate- rial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publi cdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data