Tagging a Corpus of Spoken Swedish

In this article, we present and evaluate a method for training a statistical part-of-speech tagger on data from written language and then adapting it to the requirements of tagging a corpus of transcribed spoken language, in our case spoken Swedish. This is currently a significant problem for many research groups working with spoken language, since the availability of tagged training data from spoken language is still very limited for most languages. The overall accuracy of the tagger developed for spoken Swedish is quite respectable, varying from 95% to 97% depending on the tagset used. In conclusion, we argue that the method presented here gives good tagging accuracy with relatively little effort.