Semantic Tagging of French Medical Entities Using Distant Learning

In this paper we present a semantic tagger aiming to detect relevant entities in French medical documents and tagging them with their appropriate semantic class. These experiments has been carried out in the framework of CLEF2015 eHealth contest that proposes a tagset of ten classes from UMLS taxonomy. The system presented uses a set of binary classifiers, and a combination mechanisms for combining the results of the classifiers. Learning the classifiers is performed using two widely used knowledge source, one domain restricted and the other is a domain independent resource.