Handbook of formal languages, vol. 3
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Probabilistic top-down parsing and language modeling
Computational Linguistics
Natural Language Engineering
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Exploiting syntactic structure for language modeling
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Computational complexity of probabilistic disambiguation by means of tree-grammars
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Immediate-head parsing for language models
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Discriminative syntactic language modeling for speech recognition
ACL '05 Proceedings of the 43rd Annual Meeting on 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
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
Bayesian learning of a tree substitution grammar
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Natural Language Processing with Python
Natural Language Processing with Python
Inducing Tree-Substitution Grammars
The Journal of Machine Learning Research
Syntax-based language models for statistical machine translation
Syntax-based language models for statistical machine translation
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Recent approaches for building syntactic language models include the combination of Probabilistic Tree Substitution Grammars (PTSGs) and Bayesian learning methods. While PTSGs have appealing features for syntax modeling, Bayesian methods provide a framework for inducing compact grammars that do not overfit the training corpus. In this paper, we apply these approaches to learn syntactic language models from a Brazilian Portuguese treebank.