  Words unknown to the lexicon present a substantial problem to part-of-speech
tagging. In this paper we present a technique for fully unsupervised
statistical acquisition of rules which guess possible parts-of-speech for
unknown words. Three complementary sets of word-guessing rules are induced from
the lexicon and a raw corpus: prefix morphological rules, suffix morphological
rules and ending-guessing rules. The learning was performed on the Brown Corpus
data and rule-sets, with a highly competitive performance, were produced and
compared with the state-of-the-art.
