Probabilistic Tagging with Feature Structures

Details Probabilistic Tagging with Feature Structures Probabilistic Tagging with Feature Structures

1994-10-25

arxiv.org/abs/cmp-lg/9410027v1

COLING-94, vol.1, pp.161-165, Kyoto, Japan. August 5-9, 1994.

Computer Science

Computation and Language

Coling-94, 85 KB, 5 pages

Scientific paper

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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