Language-independent automatic acquisition of morphological knowledge from synonym pairs

Medical words exhibit a rich and productive morphol-ogy. Beyond simple inflection, derivation and compo-sition are a common way to form new words. Morpho-logical knowledge is therefore very important for any medical language processing application. Whereas rich morphological resources are available for the English medical language with the UMLS Specialist Lexicon, no such resources are publicly available for French or most other languages. We propose a sim-ple and powerful method to help acquire automatically such knowledge. This method takes advantage of the synonym terms present in medical terminologies. In a bootstrapping step, it detects morphologically related words from which it learns “derivation rules”. In an expansion step, it then applies these rules to the whole vocabulary available. Our goal is to acquire data for French and other languages for which they are not available. However, to evaluate the efficiency of the method, we tested it on English in a setting which is close to that prevailing for French, and we confronted its results to those obtained with the Specialist lexical variant generation tool