Hulat-TaskAB at eHealth-KD Challenge 2019. Knowledge Recognition from Health Documents by BiLSTM-CRF.

System participating in the eHealth-KD Challenge 2019.

Abstract

Currently, the number of electronic health documents is increasing exponentially. Due to this, there is a growing interest in developing automatic systems to extract interesting information from these texts. In this paper, we describe a deep learning architecture for the identification and classification of named entities of interest in health documents. The architecture consists of two bidirectional Long Short-Term Memory layers and a final layer based on Conditional Random Fields. Our system (Hulat-TaskAB) participated in the ehealthkd-2019 sub-task A and obtained a micro-F1 of 76.63%.

NeuroNER tool

Dernoncourt, F., Lee, J.Y., Szolovits, P.: Neuroner: an easy-to-use program for named-entity recognition based on neural networks. arXiv preprint arXiv:1705.05487 (2017)

Reference

Colón-Ruiz C, Segura-Bedmar I (2019). Hulat-TaskAB at eHealth-KD Challenge 2019: Knowledge Recognition from Health Documents by BiLSTM-CRF. Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2019). Bilbao, Spain. 2019, Septiembre. CEUR, ISSN 1613-0073. 2421, 35 - 42.