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
Bayesian learning of probabilistic language models
Bayesian learning of probabilistic language models
An efficient context-free parsing algorithm
Communications of the ACM
The theory of parsing, translation, and compiling
The theory of parsing, translation, and compiling
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Tree-bank Grammars
A hybrid language model based on a combination of N-grams and stochastic context-free grammars
ACM Transactions on Asian Language Information Processing (TALIP)
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In this paper, a new method for modelling tRNA secondary structures is presented. This method is based on the combination of stochastic context-free grammars (SCFG) and Hidden Markov Models (HMM). HMM are used to capture the local relations in the loops of the molecule (nonstructured regions) and SCFG are used to capture the long term relations between nucleotides of the arms (structured regions). Given annotated public databases, the HMM and SCFG models are learned by means of automatic inductive learning methods. Two SCFG learning methods have been explored. Both of them take advantage of the structural information associated with the training sequences: one of them is based on a stochastic version of the Sakakibara algorithm and the other one is based on a Corpus based algorithm. A final model is then obtained by merging of the HMM of the nonstructured regions and the SCFG of the structured regions. Finally, the performed experiments on the tRNA sequence corpus and the non-tRNA sequence corpus give significant results. Comparative experiments with another published method are also presented.