A Benchmark Evaluation of Clinical Named Entity Recognition in French
<|reference_start|>A Benchmark Evaluation of Clinical Named Entity Recognition in French: Background: Transformer-based language models have shown strong performance on many Natural LanguageProcessing (NLP) tasks. Masked Language Models (MLMs) attract sustained interest because they can be adaptedto different languages and sub-domains through training or fine-tuning on specific corpora while remaining lighterthan modern Large Language Models (LLMs). Recently, several MLMs have been released for the biomedicaldomain in French, and experiments suggest that they outperform standard French counterparts. However, nosystematic evaluation comparing all models on the same corpora is available. Objective: This paper presentsan evaluation of masked language models for biomedical French on the task of clinical named entity recognition.Material and methods: We evaluate biomedical models CamemBERT-bio and DrBERT and compare them tostandard French models CamemBERT, FlauBERT and FrALBERT as well as multilingual mBERT using three publicallyavailable corpora for clinical named entity recognition in French. The evaluation set-up relies on gold-standardcorpora as released by the corpus developers. Results: Results suggest that CamemBERT-bio outperformsDrBERT consistently while FlauBERT offers competitive performance and FrAlBERT achieves the lowest carbonfootprint. Conclusion: This is the first benchmark evaluation of biomedical masked language models for Frenchclinical entity recognition that compares model performance consistently on nested entity recognition using metricscovering performance and environmental impact.<|reference_end|>
@article{bannour2024a,
    title={A Benchmark Evaluation of Clinical Named Entity Recognition in French},
    author={Nesrine Bannour (STL), Christophe Servan (STL), Aur'elie N'ev'eol
  (STL), Xavier Tannier (LIMICS)},
    journal={The 2024 Joint International Conference on Computational
  Linguistics, Language Resources and Evaluation (LREC-COLING 2024), May 2024,
  Torino, Italy},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19726},
    primaryClass={cs.CL cs.AI q-bio.QM}
}