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On the estimation of `small' probabilities by leaving-one-out

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3 Author(s)
Ney, H. ; Lehrstuhl fur Inf., Tech. Hochschule Aachen, Germany ; Essen, U. ; Kneser, R.

We apply the leaving-one-out concept to the estimation of `small' probabilities, i.e., the case where the number of training samples is much smaller than the number of possible classes. After deriving the Turing-Good formula in this framework, we introduce several specific models in order to avoid the problems of the original Turing-Good formula. These models are the constrained model, the absolute discounting model and the linear discounting model. These models are then applied to the problem of bigram-based stochastic language modeling. Experimental results are presented for a German and an English corpus

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:17 ,  Issue: 12 )