Unsupervised acquisition of concatenative morphology

Among the linguistic resources formalizing a language, morphological rules are among those that can be achieved in a reasonable time. Nevertheless, since the construction of such resource can require linguistic expertise, morphological rules are still lacking for many languages. The automatized acquisition of morphology is thus an open topic of interest within the NLP field. We present an approach that allows to automatically compute, from raw corpora, a data-representative description of the concatenative mechanisms of a morphology. Our approach takes advantage of phenomena that are observable for all languages using morphological inflection and derivation but are more easy to exploit when dealing with concatenative mechanisms. Since it has been developed toward the objective of being used on as many languages as possible, applying this approach to a varied set of languages needs very few expert work. The results obtained for our first participation in the 2010 edition of MorphoChallenge have confirmed both the practical interest and the potential of the method.