  The described tagger is based on a hidden Markov model and uses tags composed
of features such as part-of-speech, gender, etc. The contextual probability of
a tag (state transition probability) is deduced from the contextual
probabilities of its feature-value-pairs. This approach is advantageous when
the available training corpus is small and the tag set large, which can be the
case with morphologically rich languages.
