Internal and external tagsets in part-of-speech tagging

We present an approach to statistical partof-speech tagging that uses two di erent tagsets, one for its internal and one for its external representation. The internal tagset is used in the underlying Markov model, while the external tagset constitutes the output of the tagger. The internal tagset can be modi ed and optimized to increase tagging accuracy (with respect to the external tagset). We evaluate this approach in an experiment and show that it performs signi cantly better than approaches using only one tagset.