Une approche hybride pour la détection automatique des relations sémantiques entre entités médicales

In this paper we tackle semantic relationships extraction from medical texts. We focus on the relations that may occur between Diseases and Treatments. We propose an approach relying on two different techniques to extract the target relations: (i) relation patterns based on human expertise and (ii) machine learning based on SVM classification. This approach takes advantage of the two techniques, relying more on manual patterns when few relation samples are available and more on feature values when sufficient examples are available. Experimentations show that our approach obtains an overall 94.07% F-measure for the extraction of cure, prevent and side effect relations.

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