Consuegra-Ayala, Juan Pablo, Palomar, Manuel
UH-MatCom at eHealth-KD Challenge 2020: Deep-Learning and Ensemble Models for Knowledge Discovery in Spanish Documents
Consuegra-Ayala, Juan Pablo; Palomar, Manuel. “UH-MatCom at eHealth-KD Challenge 2020: Deep-Learning and Ensemble Models for Knowledge Discovery in Spanish Documents”. In: Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020). CEUR Workshop Proceedings, Vol-2664, pp. 112-124
URI: http://hdl.handle.net/10045/109572
DOI: 
ISSN: 1613-0073
Abstract: 
The eHealth-KD challenge hosted at IberLEF 2020 proposes a set of resources and evaluation scenarios to encourage the development of systems for the automatic extraction of knowledge from unstructured text. This paper describes the system presented by team UH-MatCom in the challenge. Several deep-learning models are trained and ensembled to automatically extract relevant entities and relations from plain text documents. State of the art techniques such as BERT, Bi-LSTM, and CRF are applied. The use of external knowledge sources such as ConceptNet is explored. The system achieved average results in the challenge, ranking fifth across all different evaluation scenarios. The ensemble method produced a slight improvement in performance. Additional work needs to be done for the relation extraction task to successfully benefit from external knowledge sources.
Keywords:eHealth, Knowledge Discovery, Natural Language Processing, Machine Learning, Entity Recognition, Relation Extraction
CEUR
info:eu-repo/semantics/conferenceObject