Towards unsupervised learning of constructions from text

Statistical learning methods offer a route for identifying linguistic constructions. Phrasal constructions are interesting both from the viewpoint of cognitive modeling and for improving NLP applications such as machine translation. In this article, an initial model structure and search algorithm for attempting to learn constructions from plain text is described. An information-theoretic optimization criteria, namely the Minimum Description Length principle, is utilized. The method is applied to a Finnish corpus consisting of stories told by children.