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dc.contributor.authorGonzález González, Jaime 
dc.contributor.authorDe Arriba Perez, Francisco 
dc.contributor.authorGarcía Méndez, Silvia 
dc.contributor.authorBusto Castiñeira, Andrea 
dc.contributor.authorGonzález Castaño, Francisco Javier 
dc.date.accessioned2023-07-11T09:49:50Z
dc.date.available2023-07-11T09:49:50Z
dc.date.issued2023-07
dc.identifier.citationJournal of King Saud University - Computer and Information Sciences, 35(7): 101634 (2023)spa
dc.identifier.issn13191578
dc.identifier.urihttp://hdl.handle.net/11093/5019
dc.description.abstractAutomatic legal text classification systems have been proposed in the literature to address knowledge extraction from judgments and detect their aspects. However, most of these systems are black boxes even when their models are interpretable. This may raise concerns about their trustworthiness. Accordingly, this work contributes with a system combining Natural Language Processing (nlp) with Machine Learning (ml) to classify legal texts in an explainable manner. We analyze the features involved in the decision and the threshold bifurcation values of the decision paths of tree structures and present this information to the users in natural language. This is the first work on automatic analysis of legal texts combining nlp and ml along with Explainable Artificial Intelligence techniques to automatically make the models’ decisions understandable to end users. Furthermore, legal experts have validated our solution, and this knowledge has also been incorporated into the explanation process as “expert-in-the-loop” dictionaries. Experimental results on an annotated data set in law categories by jurisdiction demonstrate that our system yields competitive classification performance, with accuracy values well above 90%, and that its automatic explanations are easily understandable even to non-expert users.en
dc.description.sponsorshipXunta de Galicia | Ref. ED481B-2021-118spa
dc.description.sponsorshipXunta de Galicia | Ref. ED481B-2022-093spa
dc.description.sponsorshipXunta de Galicia | Ref. ED431C 2022/04spa
dc.description.sponsorshipMinisterio de Ciencia e Innovación | Ref. TED2021-130824B-C21spa
dc.description.sponsorshipUniversidade de Vigo/CISUGspa
dc.language.isoengspa
dc.publisherJournal of King Saud University - Computer and Information Sciencesspa
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/TED2021-130824B-C21/ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleAutomatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimatorsen
dc.typearticlespa
dc.rights.accessRightsopenAccessspa
dc.identifier.doi10.1016/j.jksuci.2023.101634
dc.identifier.editorhttps://linkinghub.elsevier.com/retrieve/pii/S131915782300188Xspa
dc.publisher.departamentoEnxeñaría telemáticaspa
dc.publisher.grupoinvestigacionGrupo de Tecnoloxías da Informaciónspa
dc.subject.unesco1203.04 Inteligencia Artificialspa
dc.subject.unesco5701.04 Lingüística Informatizadaspa
dc.subject.unesco3325 Tecnología de las Telecomunicacionesspa
dc.date.updated2023-07-10T08:23:25Z
dc.computerCitationpub_title=Journal of King Saud University - Computer and Information Sciences|volume=35|journal_number=7|start_pag=101634|end_pag=spa


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