Title: French medical named entity recognition: a hybrid approach

Authors: Imane Allaouzi; Mohamed Ben Ahmed

Addresses: LIST/FSTT, Abdelmalek Essaadi University, Tangier, Morocco ' LIST/FSTT, Abdelmalek Essaadi University, Tangier, Morocco

Abstract: With the availability of a huge amount of medical textual documents in digital form, the automatic extraction of relevant information from these documents is becoming a very challenging task because of the volume and the heterogeneous structure of medical text, which contains complex vocabulary. Therefore, there is an urgent need for medical information extraction techniques. One of the most important of these techniques is named entity recognition (NER). In this paper, we propose a system of French medical NER using a hybrid approach. And since the medical domain contains various types of information, we have taken into account both clinical and biomedical data to generalise the performance of our proposed system.

Keywords: information extraction; medical named entity recognition; MNER; multi-class classification; machine learning; knowledge-based approach; unified medical language system; UMLS; natural language processing; NLP; text mining.

DOI: 10.1504/IJIE.2019.101136

International Journal of Intelligent Enterprise, 2019 Vol.6 No.2/3/4, pp.341 - 355

Received: 16 Jan 2018
Accepted: 14 Jan 2019
Published online: 24 Jul 2019 *

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