A corpus to support eHealth Knowledge Discovery technologies

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Title: A corpus to support eHealth Knowledge Discovery technologies
Authors: Piad-Morffis, Alejandro | Gutiérrez, Yoan | Muñoz, Rafael
Research Group/s: Procesamiento del Lenguaje y Sistemas de Información (GPLSI)
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Corpus | Subject-Verb-Object | Knowledge discovery | Spanish | eHealth
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: Jun-2019
Publisher: Elsevier
Citation: Journal of Biomedical Informatics. 2019, 94: 103172. doi:10.1016/j.jbi.2019.103172
Abstract: This paper presents and describes eHealth-KD corpus. The corpus is a collection of 1173 Spanish health-related sentences manually annotated with a general semantic structure that captures most of the content, without resorting to domain-specific labels. The semantic representation is first defined and illustrated with example sentences from the corpus. Next, the paper summarizes the process of annotation and provides key metrics of the corpus. Finally, three baseline implementations, which are supported by machine learning models, were designed to consider the complexity of learning the corpus semantics. The resulting corpus was used as an evaluation scenario in TASS 2018 (Martínez-Cámara et al., 2018) and the findings obtained by participants are discussed. The eHealth-KD corpus provides the first step in the design of a general-purpose semantic framework that can be used to extract knowledge from a variety of domains.
Sponsor: This research has been supported by the University of Alicante and University of Havana. Moreover, it has also been partially funded by both aforementioned universities and the Generalitat Valenciana (Conselleria d’Educació, Investigació, Cultura i Esport) through the projects PROMETEO/2018/089, PROMETEU/2018/089; Social-Univ 2.0 (ENCARGO-INTERNOOMNI-1); and PINGVALUE3-18Y.
URI: http://hdl.handle.net/10045/91553
ISSN: 1532-0464 (Print) | 1532-0480 (Online)
DOI: 10.1016/j.jbi.2019.103172
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2019 Elsevier Inc.
Peer Review: si
Publisher version: https://doi.org/10.1016/j.jbi.2019.103172
Appears in Collections:INV - GPLSI - Artículos de Revistas

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