Automatic alignment of medical vs. general terminologies

We propose an original automatic alignment of definitions taken from different dictionaries that could be associated to the same concept although they may have different labels. The alignment between a specialized terminology used by the librarians to index concepts and a general vocabulary employed by a neophyte user in order to retrieve documents on Internet, will certainly improve the performances of the information retrieval process. The selected framework is a medical one. We propose a terminology alignment by an SVM classifier trained on a compact, but relevant representation of such definition pair by several similarity measures and the length of definitions. Three syntactic levels are investigated: Nouns, Nouns-Adjectives, and Nouns-Adjectives-Verbs. Our aim is to show how the combination of similarity measures offers a better semantic access to the document content than only one measure and it improves the performances of the automatic alignment. The results obtained on the test set show the relevance of our approach, as the F-measure reaches 88%. However, this conclusion should be validated on