Automatic Acquisition of Morphological Knowledge for Medical Language Processing

Medical words exhibit a rich and productive morphology. Beyond simple in ection, derivation and composition are a common way to form new words. Morphological knowledge is therefore very important for any medical language processing application. We propose a simple and powerful method to acquire automatically such knowledge. This method takes advantage of commonly available lists of synonym terms to bootstrap the acquisition process. We experimented it on the SNOMED International Microglossary for pathology in its French version. The families of morphologically related words that we obtained were useful for query expansion in a coding assistant. Since the method does not rely on a priori knowledge of the language, it would be applicable as well to other languages such as English.