FRASIMED: a Clinical French Annotated Resource Produced through
  Crosslingual BERT-Based Annotation Projection
Jamil Zaghir Mina Bjelogrlic Jean-Philippe Goldman Soukaïna Aananou Christophe Gaudet-Blavignac Christian Lovis
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. 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.