Probabilistic Tagging with Feature Structures

Andre Kempe

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arXiv · cmp-lg/9410027

Probabilistic Tagging with Feature Structures

Andre Kempe

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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 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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Andre Kempe. 1994-10-25. Probabilistic Tagging with Feature Structures. https://arxiv.org/abs/cmp-lg/9410027

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