Dependency parsing with latent refinements of part-of-speech tags

Thomas Müller, Richárd Farkas, Alex Judea, Helmut Schmid, Hinrich Schütze

Abstract: In this paper we propose a method to increase dependency parser performance without using additional labeled or unlabeled data by refining the layer of predicted part-of-speech (POS) tags. We perform experiments on English and German and show significant improvements for both languages. The refinement is based on generative split-merge training for Hidden Markov models (HMMs).