LANGUAGE MODEL APPROXIMATION LEARNING DEVICE, ITS METHOD AND STORAGE MEDIUM RECORDING APPROXIMATION LEARNING PROGRAM

摘要

PROBLEM TO BE SOLVED: To express by parameters of a number smaller than a true model and to learn an approximate model approximate to the true model by outputting a low-order n-gram approximate model approximate to a true n-gram model from learning data with Kullback Leibler(KL) information quantity as an evalua tion scale. SOLUTION: In the approximation learning device of a language model expressing by a parameter, an n-gram Bayesian learning means 100 receives a word group as learning data to calculate a Bayesian estimated value concerning KL information quantity with a true n-gram model corresponding to the language model to output an n-gram Bayesian estimated model. A low-order n-gram learning means 200 receives the n-gram Bayesian estimated model learned by the means 100 and calculates the low-order n-gram approximation model expressed by the number of parameters smaller than the n-gram Bayesian estimated model with the KL information quantity as the evaluating scale.