Probabilistic tagging with feature structures
André Kempe · 1994
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 probaility) 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.