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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
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/DIANN_paper8.pdf 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. This paper describes the disability detection model approaches presented by UPC’s ...
Citació
Medina, S., Turmo, J., Loharja, H., Padro, L. Semi-supervised learning for disabilities detection on English and Spanish biomedical text. A: Workshop on Evaluation of Human Language Technologies for Iberian Languages. "CEUR Workshop Proceedings". 2018, p. 66-73.
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
GPLN - Grup de Processament del Llenguatge Natural
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
TALP - Centre de Tecnologies i Aplicacions del Llenguatge i la Parla

Participants

Arxius