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).
['Thomas Mueller', 'Richárd Farkas', 'Alex Judea', 'Helmut Schmid', 'Hinrich Schuetze']
Dependency parsing with latent refinements of part-of-speech tags