Automatic stochastic tagging of natural language texts
Computational Linguistics
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The Journal of Machine Learning Research
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Natural Language Engineering
Combining distributional and morphological information for part of speech induction
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
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ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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Natural Language Engineering
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CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
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EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Weakly supervised part-of-speech tagging for morphologically-rich, resource-scarce languages
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
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EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A comparison of Bayesian estimators for unsupervised Hidden Markov Model POS taggers
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NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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
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EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
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HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Equations for part-of-speech tagging
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Simple type-level unsupervised POS tagging
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A comparison of unsupervised methods for part-of-speech tagging in Chinese
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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We propose an adaptive Bayesian hidden Markov model for fully unsupervised part-of-speech (POS) induction. The proposed model with its inference algorithm has two extensions to the first-order Bayesian HMM with Dirichlet priors. First our algorithm infers the optimal number of hidden states from the training corpus rather than fixes the dimensionality of state space beforehand. The second extension studies the Chinese unknown word processing module which measures similarities from both morphological properties and context distribution. Experimental results showed that both of these two extensions can help to find the optimal categories for Chinese in terms of both unsupervised clustering metrics and grammar induction accuracies on the Chinese Treebank.