Bayesian learning of probabilistic language models
Bayesian learning of probabilistic language models
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Inducing Probabilistic Grammars by Bayesian Model Merging
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Programming Spoken Dialogs Using Grammatical Inference
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Corpus-based grammar specialization
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
LyrebirdTM: Developing Spoken Dialog Systems Using Examples
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
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This paper presents a method for inferring reversible attribute grammars from tagged natural language sentences. Attribute grammars are a form of augmented context free grammar that assign "meaning" in the form of a data structure to a string in a context free language. The method presented in this paper has the ability to infer attribute grammars that can generate a wide range of useful data structures such as simple and structured types, lists, concatenated strings, and natural numbers. The method also presents two new forms of grammar generalisation; generalisation based upon identification of optional phrases and generalisation based upon lists. The method has been applied to and tested on the task of the rapid development of spoken dialog systems.