Statistical methods for speech recognition
Statistical methods for speech recognition
SSPR '96 Proceedings of the 6th International Workshop on Advances in Structural and Syntactical Pattern Recognition
Computation of the probability of initial substring generation by stochastic context-free grammars
Computational Linguistics
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Exploiting syntactic structure for language modeling
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
Incorporating linguistic structure into maximum entropy language models
Journal of Computer Science and Technology
A hybrid language model based on a combination of N-grams and stochastic context-free grammars
ACM Transactions on Asian Language Information Processing (TALIP)
Improvement of a Whole Sentence Maximum Entropy Language Model using grammatical features
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Using perfect sampling in parameter estimation of a whole sentence maximum entropy language model
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Estimation of stochastic context-free grammars and their use as language models
Computer Speech and Language
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This paper describes a hybrid proposal to combine n-grams and Stochastic Context-Free Grammars (SCFGs) for language modeling. A classical n-gram model is used to capture the local relations between words, while a stochastic grammatical model is considered to represent the long-term relations between syntactical structures. In order to define this grammatical model, which will be used on large-vocabulary complex tasks, a category-based SCFG and a probabilistic model of word distribution in the categories have been proposed. Methods for learning these stochastic models for complex tasks are described, and algorithms for computing the word transition probabilities are also presented. Finally, experiments using the Penn Treebank corpus improved by 30% the test set perplexity with regard to the classical n-gram models.