Knowledge based word-concept model estimation and refinement for biomedical text mining
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Knowledge based word-concept model estimation and refinement for biomedical text miningFecha de publicación
2014-12xmlui.dri2xhtml.METS-1.0.item-edition
Preprint, versió de l'autorEditor
Copyright © 2014 Elsevier Inc.Cita bibliográfica
YEPES, Antonio Jimeno; BERLANGA, Rafael. Knowledge based word-concept model estimation and refinement for biomedical text mining. Journal of biomedical informatics, 2015, 53: 300-307.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S1532046414002676Versión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number ... [+]
Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation.
In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences.
The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches. [-]
Publicado en
Journal of biomedical informatics, 2015, 53: 300-307Derechos de acceso
Copyright © 2014 Elsevier Inc.
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
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