TnT is a statistical part-of-speech (POS) tagger that performs well, at least as well as other approaches like the Maximum Entropy framework. It uses second-order Markov models and handles unknown words through suffix analysis. The tagger is efficient and accurate, outperforming other methods in some cases. It uses linear interpolation for smoothing and handles unknown words by analyzing suffixes. The tagger also incorporates capitalization information and uses beam search to improve speed. Evaluations on the NEGRA and Penn Treebank corpora show high accuracy, with TnT achieving results comparable to or better than other methods. The tagger is available for research and is effective across multiple languages. The paper highlights the importance of simple models and detailed implementation choices in achieving high accuracy. TnT's approach is efficient and effective, making it a valuable tool for POS tagging.TnT is a statistical part-of-speech (POS) tagger that performs well, at least as well as other approaches like the Maximum Entropy framework. It uses second-order Markov models and handles unknown words through suffix analysis. The tagger is efficient and accurate, outperforming other methods in some cases. It uses linear interpolation for smoothing and handles unknown words by analyzing suffixes. The tagger also incorporates capitalization information and uses beam search to improve speed. Evaluations on the NEGRA and Penn Treebank corpora show high accuracy, with TnT achieving results comparable to or better than other methods. The tagger is available for research and is effective across multiple languages. The paper highlights the importance of simple models and detailed implementation choices in achieving high accuracy. TnT's approach is efficient and effective, making it a valuable tool for POS tagging.