Paper: Probabilistic Tagging With Feature Structures

ACL ID C94-1025
Title Probabilistic Tagging With Feature Structures
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1994
Authors
  • Andre Kempe (University of Stuttgart, Stuttgart Germany)

The described tagger is b,'used on a hidden Markov model and uses tags composed of features such as part- of speech, gender, etc. 'l?he 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.