Contrastive estimation: training log-linear models on unlabeled data
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A hierarchical Bayesian language model based on Pitman-Yor processes
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Interacting sequential Monte Carlo samplers for trans-dimensional simulation
Computational Statistics & Data Analysis
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
A comparison of Bayesian estimators for unsupervised Hidden Markov Model POS taggers
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Inducing compact but accurate tree-substitution grammars
NAACL '09 Proceedings of Human Language Technologies: The 2009 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
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
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As linguistic models incorporate more subtle nuances of language and its structure, standard inference techniques can fall behind. These models are often tightly coupled such that they defy clever dynamic programming tricks. Here we demonstrate that Sequential Monte Carlo approaches, i.e. particle filters, are well suited to approximating such models. We implement two particle filters, which jointly sample either sentences or word types, and incorporate them into a Particle Gibbs sampler for Bayesian inference of syntactic part-of-speech categories. We analyze the behavior of the samplers and compare them to an exact block sentence sampler, a local sampler, and an existing heuristic word type sampler. We also explore the benefits of mixing Particle Gibbs and standard samplers.