TnT – A Statistical Part-of-Speech Tagger
TnT – A Statistical Part-of-Speech Tagger
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DOI：
10.3115/974147.974178
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发表时间：
2000-03
期刊：
影响因子：
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通讯作者：
T. Brants
中科院分区：
文献类型：
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作者：
T. Brants
TnT是一种高效的统计词性标注方法。与其他文献中发现的主张相反，我们认为基于马尔可夫模型的标记器的性能至少与其他当前的方法一样好，包括最大熵框架。最近的一项比较甚至表明，在测试的语料库中，TNT的表现要好得多。我们描述了TNT的基本模型，用于平滑和处理未知词的技术。此外，我们还对两个语料库进行了评估。
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.