Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Tagging English text with a probabilistic model
Computational Linguistics
Contrastive estimation: training log-linear models on unlabeled data
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Part of speech tagging in context
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
KU: word sense disambiguation by substitution
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
USYD: WSD and lexical substitution using the Web1T corpus
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Minimized models for unsupervised part-of-speech tagging
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
The noisy channel model for unsupervised word sense disambiguation
Computational Linguistics
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We show that unsupervised part of speech tagging performance can be significantly improved using likely substitutes for target words given by a statistical language model. We choose unambiguous substitutes for each occurrence of an ambiguous target word based on its context. The part of speech tags for the unambiguous substitutes are then used to filter the entry for the target word in the word--tag dictionary. A standard HMM model trained using the filtered dictionary achieves 92.25% accuracy on a standard 24,000 word corpus.