Papers › The Quaero French Medical Corpus: A Ressource for Medical Entity Recognition and Normalization
The Quaero French Medical Corpus: A Ressource for Medical Entity Recognition and Normalization
Cyril Grouin, Jeremy Leixa, Aurélie Névéol, Sophie Rosset, Xavier Tannier, Pierre Zweigenbaum
A vast amount of information in the biomedical domain is available as natural language free text. An increasing number of documents in the field are written in languages other than English. Therefore, it is essential to develop resources, methods and tools that address Natural Language Processing in the variety of languages used by the biomedical community. In this paper, we report on the development of an extensive corpus of biomedical documents in French annotated at the entity and concept level. Three text genres are covered, comprising a total of 103,056 words. Ten entity categories corresponding to UMLS Semantic Groups were annotated, using automatic pre-annotations validated by trained human annotators. The pre-annotation method was found helful for entities and achieved above 0.83 precision for all text genres. Overall, a total of 26,409 entity annotations were mapped to 5,797 unique UMLS concepts.
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