Probabilistic Tagging with Feature Structures

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dc.creatorKempe, Andre
dc.date1994-10-25
dc.date.accessioned2026-07-07T09:09:36Z
dc.date.available2026-07-07T09:09:36Z
dc.descriptionThe 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.
dc.descriptionColing-94, 85 KB, 5 pages
dc.identifierhttps://arxiv.org/abs/cmp-lg/9410027
dc.identifierhttp://arxiv.org/abs/cmp-lg/9410027
dc.identifierCOLING-94, vol.1, pp.161-165, Kyoto, Japan. August 5-9, 1994.
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/151092
dc.subjectComputation and Language
dc.titleProbabilistic Tagging with Feature Structures
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