On the bias of the Turing-Good estimate of probabilities

Good's (1953) estimate, based on Turing's formula, was suggested for estimating the probabilities of words in text as well as of species in a mixed population and was found particularly useful for the probability of unseen classes. The authors address the issue of bias in Good's estimate and propose an alternative to reduce this bias. This may be important in the construction of a language model for speech recognition where sparse data and low probability events are key problems.<
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