TnT - A Statistical Part-Of-Speech Tagger

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TnT - A Statistical Part-Of-Speech Tagger (2000)

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by Thorsten Brants

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540 - 5 self

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@MISC{Brants00tnt-,
    author = {Thorsten Brants},
    title = {TnT - A Statistical Part-Of-Speech Tagger},
    year = {2000}
}

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Abstract

Trigrams'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.

Keyphrases

statistical part-of-speech tagger recent comparison unknown word basic model maximum entropy framework current approach tested corpus markov model performs efficient statistical part-of-speech tagger

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