Contextual dependencies in unsupervised word segmentation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Sampling alignment structure under a Bayesian translation model
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Sampling table configurations for the hierarchical poisson-dirichlet process
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Non-parametric bayesian segmentation of Japanese noun phrases
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Unsupervised color images segmentation using spatial hidden MRF GDPM model
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Smoothing for bracketing induction
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Bayesian Constituent Context Model for Grammar Induction
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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The implementation of collapsed Gibbs samplers for non-parametric Bayesian models is non-trivial, requiring considerable book-keeping. Goldwater et al. (2006a) presented an approximation which significantly reduces the storage and computation overhead, but we show here that their formulation was incorrect and, even after correction, is grossly inaccurate. We present an alternative formulation which is exact and can be computed easily. However this approach does not work for hierarchical models, for which case we present an efficient data structure which has a better space complexity than the naive approach.