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

Autor
Medina, S.; Turmo, J.; Loharja, H.; Padro, L.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
IberEval 2018 - Third Workshop on Evaluation of Human Language Technologies for Iberian Languages
Any de l'edició
2018
Data de presentació
2018-09-18
Llibre d'actes
CEUR Workshop Proceedings
Pàgina inicial
66
Pàgina final
73
Repositori
http://hdl.handle.net/2117/124272 Obrir en finestra nova
URL
http://ceur-ws.org/Vol-2150/ Obrir en finestra nova
Resum
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. © 2018 CEUR-WS. All Rights Reserved.
Paraules clau
Biomedical abstracts, Biomedical text, Crf models, Detection models, Disabilities detection, Exact matching, Long short-term memory, Natural language processing systems, Semi- supervised learning, Semi-supervised learning Learning algorithms, Supervised learning, Word embedding
Grup de recerca
EC - Enginyeria de la Construcció
GPLN - Grup de Processament del Llenguatge Natural
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
TALP - Centre de Tecnologies i Aplicacions del Llenguatge i la Parla

Participants