Statistical methods for speech recognition
Statistical methods for speech recognition
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Contrastive estimation: training log-linear models on unlabeled data
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Prototype-driven learning for sequence models
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
The infinite HMM for unsupervised PoS tagging
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Bayesian inference for finite-state transducers
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Starting from scratch in semantic role labeling
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Improved unsupervised POS induction through prototype discovery
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
SVD and clustering for unsupervised POS tagging
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Posterior Regularization for Structured Latent Variable Models
The Journal of Machine Learning Research
Improved unsupervised POS induction using intrinsic clustering quality and a Zipfian constraint
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Type level clustering evaluation: new measures and a POS induction case study
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Crouching Dirichlet, hidden Markov model: unsupervised POS tagging with context local tag generation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Two decades of unsupervised POS induction: how far have we come?
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Latent-descriptor clustering for unsupervised POS induction
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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
A hierarchical Pitman-Yor process HMM for unsupervised part of speech induction
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Controlling complexity in part-of-speech induction
Journal of Artificial Intelligence Research
Evaluating unsupervised learning for natural language processing tasks
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Unsupervised dependency parsing without gold part-of-speech tags
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Adaptive Bayesian HMM for Fully Unsupervised Chinese Part-of-Speech Induction
ACM Transactions on Asian Language Information Processing (TALIP)
Unsupervised part of speech inference with particle filters
WILS '12 Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure
Learning syntactic categories using paradigmatic representations of word context
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Wiki-ly supervised part-of-speech tagging
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Unsupervised bayesian part of speech inference with particle gibbs
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Mining novelty-seeking trait across heterogeneous domains
Proceedings of the 23rd international conference on World wide web
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There is growing interest in applying Bayesian techniques to NLP problems. There are a number of different estimators for Bayesian models, and it is useful to know what kinds of tasks each does well on. This paper compares a variety of different Bayesian estimators for Hidden Markov Model POS taggers with various numbers of hidden states on data sets of different sizes. Recent papers have given contradictory results when comparing Bayesian estimators to Expectation Maximization (EM) for unsupervised HMM POS tagging, and we show that the difference in reported results is largely due to differences in the size of the training data and the number of states in the HMM. We invesigate a variety of samplers for HMMs, including some that these earlier papers did not study. We find that all of Gibbs samplers do well with small data sets and few states, and that Variational Bayes does well on large data sets and is competitive with the Gibbs samplers. In terms of times of convergence, we find that Variational Bayes was the fastest of all the estimators, especially on large data sets, and that explicit Gibbs sampler (both pointwise and sentence-blocked) were generally faster than their collapsed counterparts on large data sets.