Analysis of eHealth knowledge discovery systems in the TASS 2018 workshop

Alejandro Piad-Morffis, Yoan Gutiérrez, Suilan Estévez-Velarde, Yudivián Almeida-Cruz, Andrés Montoyo, Rafael Muñoz


This paper presents an analysis of Task 3 eHealth-KD challenge in the TASS 2018Workshop. The challenge consisted of the extraction of concepts, actions, and their corresponding semantic relations from health-related documents written in the Spanish language. The documents were manually annotated with a schema based on triples (Subject, Action, Target) and an additional set of semantic relations. Several research teams presented computational systems, obtaining relevant results in different subtasks. In this paper, the approaches performed by each team are analyzed and the most promising lines for future development are highlighted and discussed. Moreover, an in-depth analysis of the results is presented focusing on the main characteristics of each subtask. The overall eHealth-KD analysis has indicated that the Knowledge Discovery (KD) task, speciffically focused on concrete domains and languages, represents a rich area for further research. In addition, this study considers that the fusion of machine learning {especially deep learning techniques{ and knowledge-based approaches will benefit the KD task. Keywords: Machine learning, natural language processing, knowledge bases, knowledge discovery, eHealth

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