TnT -- A Statistical Part-of-Speech Tagger

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 significantlybetter 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.

• Type: Conference
• Year: 2000
• Where: ANLP
• Authors: Thorsten Brants
• Keywords: ANLP 2000, Maximum Entropy Framework, Statistical Part-of-speech Tagger, TnT Performs Significantlybetter