Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Inducing Probabilistic Grammars by Bayesian Model Merging
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
A fast method for statistical grammar induction
Natural Language Engineering
Parsing the Wall Street Journal with the inside-outside algorithm
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
Bayesian grammar induction for language modeling
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Inside-outside reestimation from partially bracketed corpora
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
A generative constituent-context model for improved grammar induction
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Online Model Selection Based on the Variational Bayes
Neural Computation
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Refining the structure of a stochastic context-free grammar
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Unsupervised Learning of Probabilistic Context-Free Grammar using Iterative Biclustering
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Variational Bayes via propositionalized probability computation in PRISM
Annals of Mathematics and Artificial Intelligence
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Shared logistic normal distributions for soft parameter tying in unsupervised grammar induction
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Covariance in Unsupervised Learning of Probabilistic Grammars
The Journal of Machine Learning Research
Semantic parsing with Bayesian tree transducers
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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This paper presents a new grammar induction algorithm for probabilistic context-free grammars (PCFGs). There is an approach to PCFG induction that is based on parameter estimation. Following this approach, we apply the variational Bayes to PCFGs. The variational Bayes (VB) is an approximation of Bayesian learning. It has been empirically shown that VB is less likely to cause overfitting. Moreover, the free energy of VB has been successfully used in model selection. Our algorithm can be seen as a generalization of PCFG induction algorithms proposed before. In the experiments, we empirically show that induced grammars achieve better parsing results than those of other PCFG induction algorithms. Based on the better parsing results, we give examples of recursive grammatical structures found by the proposed algorithm.