Semi-supervised learning for disabilities detection on English and Spanish biomedical text

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Title:	Semi-supervised learning for disabilities detection on English and Spanish biomedical text
Author:	Medina Herrera, Salvador; Turmo Borras, Jorge; Loharja, Henry; Padró, Lluís
Other authors:	Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
Abstract:	This paper describes the disability detection model approaches presented by UPC’s TALP 3 team for the DIANN 2018 shared task. The best of those approaches was ranked in 3rd place for exact-matching of disability detection. The models combine a semi-supervised learning model using CRFs and LSTM with word embedding features with a supervised CRF model for the detection of disabilities and negations respectively.
Abstract:	Peer Reviewed
Subject(s):	-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic -Supervised learning (Machine learning) -disabilities detection -biomedical abstracts -semi-supervised learning -Aprenentatge automàtic
Rights:	Attribution 3.0 Spain http://creativecommons.org/licenses/by/3.0/es/
Document type:	Article - Published version Conference Object
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