A Hybrid Approach for French Medical Entity Recognition and Normalization

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 37)

Abstract

Medical document written in natural language is available in electronic form, and it constitutes an invaluable source for medical research. This paper describes our system based on hybrid approach for the task of Named Entity Recognition and Normalization of French medical documents using QUAERO corpus [1]. To evaluate our system, we took part in three subtasks: Entity Normalization, Named Entity Extraction and Classification which involved 10 categories including Anatomy, Chemicals & Drugs, Devices, Disorders, Geographic Areas, Living Beings, Objects, Phenomena, Physiology and Procedures. The results on both tasks, Named Entity Recognition and Normalization, demonstrate high performance as compared to other methods for French Medical Entity Recognition and Normalization.

Keywords

Medical entity recognition Automatic categorization Normalization UMLS Machine learning Knowledge-based NLP 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.LIST/FSTTAbdelmalek Essaadi UniversityTangierMorocco

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