Contextualized French Language Models for Biomedical Named Entity Recognition - Archive ouverte HAL Access content directly
Conference Papers Year : 2020

Contextualized French Language Models for Biomedical Named Entity Recognition

Jenny Copara
  • Function : Author
  • PersonId : 1072399
Julien Knafou
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  • PersonId : 1072400
Nona Naderi
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  • PersonId : 1072401
Claudia Moro
  • Function : Author
  • PersonId : 1072402
Patrick Ruch
  • Function : Author
  • PersonId : 1072403


Named entity recognition (NER) is key for biomedical applications as it allows knowledge discovery in free text data. As entities are semantic phrases, their meaning is conditioned to the context to avoid ambiguity. In this work, we explore contextualized language models for NER in French biomedical text as part of the Défi Fouille de Textes challenge. Our best approach achieved an F1 -measure of 66% for symptoms and signs, and pathology categories, being top 1 for subtask 1. For anatomy, dose, exam, mode, moment, substance, treatment, and value categories, it achieved an F1 -measure of 75% (subtask 2). If considered all categories, our model achieved the best result in the challenge, with an F1 -measure of 72%. The use of an ensemble of neural language models proved to be very effective, improving a CRF baseline by up to 28% and a single specialised language model by 4%.
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Dates and versions

hal-02784740 , version 1 (05-06-2020)
hal-02784740 , version 2 (17-06-2020)
hal-02784740 , version 3 (23-06-2020)


  • HAL Id : hal-02784740 , version 3


Jenny Copara, Julien Knafou, Nona Naderi, Claudia Moro, Patrick Ruch, et al.. Contextualized French Language Models for Biomedical Named Entity Recognition. 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Atelier DÉfi Fouille de Textes, 2020, Nancy, France. pp.36-48. ⟨hal-02784740v3⟩
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