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How to Adapt Deep Learning Models to a New Domain: The Case of Biomedical Relation Extraction

Cómo adaptar un modelo de aprendizaje profundo a un nuevo dominio: el caso de la extracción de relaciones biomédicas

dc.creatorPeña-Torres, Jefferson A.
dc.creatorGutiérrez, Raúl E.
dc.creatorBucheli, Víctor A.
dc.creatorGonzález, Fabio A.
dc.date2019-12-05
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1483
dc.identifier10.22430/22565337.1483
dc.descriptionIn this article, we study the relation extraction problem from Natural Language Processing (NLP) implementing a domain adaptation setting without external resources. We trained a Deep Learning (DL) model for Relation Extraction (RE), which extracts semantic relations in the biomedical domain. However, can the model be applied to different domains? The model should be adaptable to automatically extract relationships across different domains using the DL network. Completely training DL models in a short time is impractical because the models should quickly adapt to different datasets in several domains without delay. Therefore, adaptation is crucial for intelligent systems, where changing factors and unanticipated perturbations are common. In this study, we present a detailed analysis of the problem, as well as preliminary experimentation, results, and their evaluation.en-US
dc.descriptionEn este trabajo estudiamos el problema de extracción de relaciones del Procesamiento de Lenguaje Natural (PLN). Realizamos una configuración para la adaptación de dominio sin recursos externos. De esta forma, entrenamos un modelo con aprendizaje profundo (DL) para la extracción de relaciones (RE). El modelo permite extraer relaciones semánticas para el dominio biomédico. Sin embargo, ¿El modelo puede ser aplicado a diferentes dominios? El modelo debería adaptarse automáticamente para la extracción de relaciones entre diferentes dominios usando la red de DL. Entrenar completamente modelos DL en una escala de tiempo corta no es práctico, deseamos que los modelos se adapten rápidamente de diferentes conjuntos de datos con varios dominios y sin demora. Así, la adaptación es crucial para los sistemas inteligentes que operan en el mundo real, donde los factores cambiantes y las perturbaciones imprevistas son habituales. En este artículo, presentamos un análisis detallado del problema, una experimentación preliminar, resultados y la discusión acerca de los resultados.es-ES
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dc.languageeng
dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1483/1472
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1483/1562
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1483/1567
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dc.rightsCopyright (c) 2019 TecnoLógicasen-US
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0en-US
dc.sourceTecnoLógicas; Vol. 22 (2019): Special issue-2019; 49-62en-US
dc.sourceTecnoLógicas; Vol. 22 (2019): Edición especial-2019; 49-62es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectSemantic Extractionen-US
dc.subjectDeep Learningen-US
dc.subjectRelation Extractionen-US
dc.subjectNatural Language Processingen-US
dc.subjectExtracción semánticaes-ES
dc.subjectAprendizaje profundoes-ES
dc.subjectExtracción de relacioneses-ES
dc.subjectProcesamiento de lenguaje naturales-ES
dc.titleHow to Adapt Deep Learning Models to a New Domain: The Case of Biomedical Relation Extractionen-US
dc.titleCómo adaptar un modelo de aprendizaje profundo a un nuevo dominio: el caso de la extracción de relaciones biomédicases-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES


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