  This paper describes the conversion of a Hidden Markov Model into a
sequential transducer that closely approximates the behavior of the stochastic
model. This transformation is especially advantageous for part-of-speech
tagging because the resulting transducer can be composed with other transducers
that encode correction rules for the most frequent tagging errors. The speed of
tagging is also improved. The described methods have been implemented and
successfully tested on six languages.
