Vicomtech at eHealth-KD Challenge 2020: Deep End-to-End Model for Entity and Relation Extraction in Medical Text
Egileak: Naiara Perez Miguel
Data: 23.09.2020
Abstract
This paper describes the participation of the Vicomtech NLP team in the eHealth-KD 2020 shared task about detecting and classifying entities and relations in health-related texts written in Spanish. The proposed system consists of a single end-to-end deep neural network with pre-trained BERT models as the core for the semantic representation of the input texts. We have experimented with two models: BERT-Base Multilingual Cased and BETO, a BERT model pre-trained on Spanish text. Our system models all the output variables---entities and relations---at the same time, modelling the whole problem jointly. Some of the outputs are fed back to latter layers of the model, connecting the outcomes of the different subtasks in a pipeline fashion. Our system shows robust results in all the scenarios of the task. It has achieved the first position in the main scenario of the competition and top-3 in the rest of the scenarios.
BIB_text
title = {Vicomtech at eHealth-KD Challenge 2020: Deep End-to-End Model for Entity and Relation Extraction in Medical Text},
pages = {102-111},
keywds = {
Entity detection, Relation extraction, Health documents
}
abstract = {
This paper describes the participation of the Vicomtech NLP team in the eHealth-KD 2020 shared task about detecting and classifying entities and relations in health-related texts written in Spanish. The proposed system consists of a single end-to-end deep neural network with pre-trained BERT models as the core for the semantic representation of the input texts. We have experimented with two models: BERT-Base Multilingual Cased and BETO, a BERT model pre-trained on Spanish text. Our system models all the output variables---entities and relations---at the same time, modelling the whole problem jointly. Some of the outputs are fed back to latter layers of the model, connecting the outcomes of the different subtasks in a pipeline fashion. Our system shows robust results in all the scenarios of the task. It has achieved the first position in the main scenario of the competition and top-3 in the rest of the scenarios.
}
date = {2020-09-23},
}
