Towards Deep Universal Dependencies

Abstract

Many linguistic theories and annotation frameworks contain a deep-syntactic and/or semantic layer. While many of these frameworks have been applied to more than one language, none of them is anywhere near the number of languages that are covered in Universal Dependencies (UD). In this paper, we present a prototype of Deep Universal Dependencies, a two-speed concept where minimal deep annotation can be derived automatically from surface UD trees, while richer annotation can be added for datasets where appropriate resources are available. We release the Deep UD data in Lindat.