ML p(r)ior | Probabilistic Tagging with Feature Structures
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Probabilistic Tagging with Feature Structures

1994-10-25
9410027 | cmp-lg
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.
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