An Efficient, Probabilistically Sound Algorithm for Segmentation andWord Discovery
Machine Learning - Special issue on natural language learning
Inducing Probabilistic Grammars by Bayesian Model Merging
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Monte Carlo Statistical Methods (Springer Texts in Statistics)
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Variational inference for adaptor grammars
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Painless unsupervised learning with features
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ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Blocked inference in Bayesian tree substitution grammars
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CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
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EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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Unsupervised phonemic Chinese word segmentation using adaptor grammars
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Inducing Tree-Substitution Grammars
The Journal of Machine Learning Research
Insertion operator for Bayesian tree substitution grammars
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Modeling infant word segmentation
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
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The Journal of Machine Learning Research
Incorporating lexical priors into topic models
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Using rejuvenation to improve particle filtering for Bayesian word segmentation
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Unsupervised morphology rivals supervised morphology for Arabic MT
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
A regularized compression method to unsupervised word segmentation
SIGMORPHON '12 Proceedings of the Twelfth Meeting of the Special Interest Group on Computational Morphology and Phonology
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EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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One of the reasons nonparametric Bayesian inference is attracting attention in computational linguistics is because it provides a principled way of learning the units of generalization together with their probabilities. Adaptor grammars are a framework for defining a variety of hierarchical nonparametric Bayesian models. This paper investigates some of the choices that arise in formulating adaptor grammars and associated inference procedures, and shows that they can have a dramatic impact on performance in an unsupervised word segmentation task. With appropriate adaptor grammars and inference procedures we achieve an 87% word token f-score on the standard Brent version of the Bernstein-Ratner corpus, which is an error reduction of over 35% over the best previously reported results for this corpus.