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 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. we achieve the automated tagging of a syntactic-structure-based set of grammatical function tags including phrase-chunk and syntactic-role modifiers trained in supervised mode from a tree bank of german.