TnT - A Statistical Part-of-Speech Tagger

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dc.creatorBrants, Thorsten
dc.date2000-03-13
dc.date.accessioned2026-07-07T03:16:04Z
dc.date.available2026-07-07T03:16:04Z
dc.descriptionTrigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison has even shown that TnT performs significantly better for the tested corpora. We describe the basic model of TnT, the techniques used for smoothing and for handling unknown words. Furthermore, we present evaluations on two corpora.
dc.description8 pages
dc.identifierhttps://arxiv.org/abs/cs/0003055
dc.identifierhttp://arxiv.org/abs/cs/0003055
dc.identifierProceedings of ANLP-2000, Seattle, WA
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30215
dc.subjectComputation and Language
dc.subjectI.2.7
dc.titleTnT - A Statistical Part-of-Speech Tagger
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