Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
IEEE Transactions on Pattern Analysis and Machine Intelligence
Procedure for quantitatively comparing the syntactic coverage of English grammars
HLT '91 Proceedings of the workshop on Speech and Natural Language
Elements of information theory
Elements of information theory
Efficient learning of context-free grammars from positive structural examples
Information and Computation
An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
Minimal Ascending and Descending Tree Automata
SIAM Journal on Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing the relative entropy between regular tree languages
Information Processing Letters
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Probabilistic Languages: A Review and Some Open Questions
ACM Computing Surveys (CSUR)
Statistical Language Learning
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Stochastic Inference of Regular Tree Languages
Machine Learning
On the Estimation of 'Small' Probabilities by Leaving-One-Out
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transformation of Documents and Schemas by Patterns and Contextual Conditions
PODP '96 Proceedings of the Third International Workshop on Principles of Document Processing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Estimation of probabilistic context-free grammars
Computational Linguistics
PCFG models of linguistic tree representations
Computational Linguistics
Compacting the Penn Treebank grammar
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Inside-outside reestimation from partially bracketed corpora
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Towards history-based grammars: using richer models for probabilistic parsing
HLT '91 Proceedings of the workshop on Speech and Natural Language
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Bottom-up generative modeling of tree-structured data
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Classifying melodies using tree grammars
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Locality and the complexity of minimalist derivation tree languages
FG'10/FG'11 Proceedings of the 15th and 16th international conference on Formal Grammar
Theory of Computing Systems
Theory of Computing Systems
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Probabilistic k-testable models (usually known as k-gram models in the case of strings) can be easily identified from samples and allow for smoothing techniques to deal with unseen events during pattern classification. In this paper, we introduce the family of stochastic k-testable tree languages and describe how these models can approximate any stochastic rational tree language. The model is applied to the task of learning a probabilistic k-testable model from a sample of parsed sentences. In particular, a parser for a natural language grammar that incorporates smoothing is shown.