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- W2159782014 abstract "The language model probabilities are estimated by an empirical Bayes approach in which a prior distribution for the unknown probabilities is itself estimated through a novel choice of data. The predictive power of the model thus fitted is compared by means of its experimental perplexity [1] to the model as fitted by the Jelinek-Mercer deleted estimator and as fitted by the Turing-Good formulas for probabilities of unseen or rarely seen events." @default.
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- W2159782014 title "Estimation of probabilities in the language model of the IBM speech recognition system" @default.
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- W2159782014 doi "https://doi.org/10.1109/tassp.1984.1164378" @default.
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