TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Comparing a linguistic and a stochastic tagger
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Serial combination of rules and statistics: a case study in Czech tagging
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
You don’t have to think twice if you carefully tokenize
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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In this paper, we present techniques aimed at avoiding typical errors of state-of-the-art POS-taggers and at constructing high-quality POS-taggers with extremely low error rates. Such taggers are very helpful, if not even necessary, for many NLP applications organized in a pipeline architecture. The appropriateness of the suggested solutions is demonstrated in several experiments. Although these experiments were performed only with German data, the proposed modular architecture is applicable for many other languages, too.