  One of the problems in part-of-speech tagging of real-word texts is that of
unknown to the lexicon words. In Mikheev (ACL-96 cmp-lg/9604022), a technique
for fully unsupervised statistical acquisition of rules which guess possible
parts-of-speech for unknown words was proposed. One of the over-simplification
assumed by this learning technique was the acquisition of morphological rules
which obey only simple concatenative regularities of the main word with an
affix. In this paper we extend this technique to the non-concatenative cases of
suffixation and assess the gain in the performance.
