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

TL;DR

This paper presents a crosslingual BERT-based annotation projection method to efficiently generate high-quality annotated datasets for low-resource languages, demonstrated by creating the large French clinical NLP corpus FRASIMED.

Contribution

It introduces a novel crosslingual annotation projection approach using BERT, enabling the creation of large annotated datasets with minimal human effort.

Findings

High accuracy in dataset annotation demonstrated

Effective increase of low-resource corpora with minimal effort

FRASIMED is the largest open annotated French medical corpus

Abstract

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.