Clinical Case Reports for NLP
Cyril Grouin, Natalia Grabar, Vincent Claveau, Thierry Hamon · 2019
Textual data are useful to access expert information.Since the texts are representative of distinct language uses, it is necessary to build specific corpora in order to be able to design suitable NLP tools.In some domains, such as medical domain, it may be complicated to access the representative textual data and their semantic annotations, while there exists a real need for providing efficient tools and methods.In this paper, we present a corpus of 717 clinical cases written in French.We manually annotated this corpus into four general categories (age, gender, outcome, and origin) for a total number of 2,835 annotations.The values of age, gender, and outcome have been normalized.We also manually annotated a subset of 70 files into 27 fine-grained categories, for a total number of 5,198 annotations.In addition, we present a few basic experiments made on those annotations in order to highlight their usefulness.