FRASIMED : A Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection

Conférence Chapitre d'actes FRASIMED : A Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection eng 2024-05 author Zaghir Jamil 0000-0002-8209-6098 956784 Département de radiologie et informatique médicale (RADIO) author Bjelogrlic Mina 0000-0002-6922-3283 918489 Département de radiologie et informatique médicale (RADIO) author Goldman Jean-Philippe 0000-0003-4000-4199 98472 Département de radiologie et informatique médicale (RADIO) author Aananou Soukaïna 412535 author Gaudet-Blavignac Christophe 0000-0001-6527-5898 645533 author Lovis Christian 0000-0002-2681-8076 29016 Département de radiologie et informatique médicale (RADIO) 7450-7460 Natural language processing (NLP) applications such as named entity recognition (NER) for low-resource corpora do not benefit from recent advances in the development of large language models (LLMs) where there is still a need for larger annotated datasets. This research article introduces a methodology for generating translated versions of annotated datasets through crosslingual annotation projection and is freely available on GitHub (link: https://github.com/JamilProg/crosslingual_bert_annotation_projection). Leveraging a language agnostic BERT-based approach, it is an efficient solution to increase low-resource corpora with few human efforts and by only using already available open data resources. Quantitative and qualitative evaluations are often lacking when it comes to evaluating the quality and effectiveness of semi-automatic data generation strategies. The evaluation of our crosslingual annotation projection approach showed both effectiveness and high accuracy in the resulting dataset. As a practical application of this methodology, we present the creation of French Annotated Resource with Semantic Information for Medical Entities Detection (FRASIMED), an annotated corpus comprising 2’051 synthetic clinical cases in French. The corpus is now available for researchers and practitioners to develop and refine French natural language processing (NLP) applications in the clinical field (https://zenodo.org/record/8355629), making it the largest open annotated corpus with linked medical concepts in French. Annotation projection Crosslingual Medical entity recognition Entity linking Large medical annotated dataset Dewey 616.0757 ELRA; ICCL Torino Swiss National Science Foundation NCCR Evolving Language (phase I) 180888 CH Programmes 62 Département de radiologie et informatique médicale 227 false 344 Interfaces Homme-machine en milieu clinique 610 https://zenodo.org/records/8355629 https://github.com/JamilProg/crosslingual_bert_annotation_projection Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, and Nianwen Xue 20-25 May 2024 Torino 7d503c26-a413-4544-b076-9cef4e228238 Proceedings chapter (Published version) Public 2024.lrec-main.657.pdf 1305903 application/pdf CC BY-NC-4.0 c6d737a5966f8a4e92115d484a1c42d63794498a49a5326219b6c3f3de89a28c openAccess Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale

Title	Published in	Access level	OA Policy	Year	Views	Downloads

FRASIMED : A Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection	Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)			2024	218	420

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Aananou, Soukaïna