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Talp-UPC at eHealth-KD challenge 2019: A joint model with contextual embeddings for clinical information extraction

Author
Medina, S.; Turmo, J.
Type of activity
Presentation of work at congresses
Name of edition
Iberian Languages Evaluation Forum 2019
Date of publication
2019
Presentation's date
2019-09-24
Book of congress proceedings
Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2019): co-located with 35th Conference of the Spanish Society for Natural Language Processing (SEPLN 2019): Bilbao, Spain, September 24th, 2019
First page
78
Last page
84
Project funding
Semantic graph extraction from textual health histories
Repository
http://hdl.handle.net/2117/169003 Open in new window
URL
http://ceur-ws.org/Vol-2421/eHealth-KD_paper_8.pdf Open in new window
Abstract
Most eHealth entity recognition and relation extraction models tackle the identification of entities and relations with independent specialized models. In this article, we show how a single combined model can exploit the correlation between these two tasks to improve the evaluation score of both, while reducing training and execution time. Our model uses both traditional part-of-speech tagging and dependency-parsing of the documents and state-of-the-art pre-trained Contextual Embeddings as input...
Keywords
Contextual embeddings, NERC, Relation extraction, eHealth NLP
Group of research
GPLN - Natural Language Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
TALP - Centre for Language and Speech Technologies and Applications

Participants