Publications
Authors:
  • Andre Kempe
Citation:
Proc. ACL'97, Madrid, Spain, pp. 460-467
Abstract:
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.
Year:
1997
Report number:
1997/002
Attachments: