  In this paper we present the results of comparing a statistical tagger for
German based on decision trees and a rule-based Brill-Tagger for German. We
used the same training corpus (and therefore the same tag-set) to train both
taggers. We then applied the taggers to the same test corpus and compared their
respective behavior and in particular their error rates. Both taggers perform
similarly with an error rate of around 5%. From the detailed error analysis it
can be seen that the rule-based tagger has more problems with unknown words
than the statistical tagger. But the results are opposite for tokens that are
many-ways ambiguous. If the unknown words are fed into the taggers with the
help of an external lexicon (such as the Gertwol system) the error rate of the
rule-based tagger drops to 4.7%, and the respective rate of the statistical
taggers drops to around 3.7%. Combining the taggers by using the output of one
tagger to help the other did not lead to any further improvement.
