Probabilistic Tagging with Feature Structures
<|reference_start|>Probabilistic Tagging with Feature Structures: 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.<|reference_end|>
@article{kempe1994probabilistic,
    title={Probabilistic Tagging with Feature Structures},
    author={Andre Kempe (University of Stuttgart)},
    journal={COLING-94, vol.1, pp.161-165, Kyoto, Japan. August 5-9, 1994.},
    year={1994},
    archivePrefix={arXiv},
    eprint={cmp-lg/9410027},
    primaryClass={cmp-lg cs.CL}
}