Probabilistic tagging with feature structures

Authors:
André Kempe
Affiliations:
University of Stuttgart, Stuttgart, Germany
Venue:
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Year:
1994

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Abstract

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.