Stagger: A modern POS tagger for Swedish

The field of Part of Speech (POS) tagging has made slow but steady progress during the last decade, though many of the new methods developed have not previously been applied to Swedish. I present a new system, based on the Averaged Perceptron algorithm and semi-supervised learning, that is more accurate than previous Swedish POS taggers. Furthermore, a new version of the Stockholm-Umeå Corpus is presented, whose more consistent annotation leads to significantly lower error rates for the POS tagger. Finally, a new, freely available annotated corpus of Swedish blog posts is presented and used to evaluate the tagger’s accuracy on this increasingly important genre. Details of the evaluation are presented throughout, to ensure easy comparison with future results.