TnT

TLDR

Trigrams'n'Tags (TnT) is an efficient statistical part‑of‑speech tagger. TnT employs a trigram Markov model with smoothing and unknown‑word handling, and its performance was evaluated on two corpora. Markov‑model taggers, including TnT, match or exceed other approaches, with TnT outperforming competitors on the evaluated corpora.

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.