Learning theory and language modeling
We consider some of our recent work on Good-Turing estimators in the larger context of learning theory and language modeling. The Good-Turing estimators have played a significant role in natural language modeling for the past 20 years. We have recently shown that these particular leave-one-out estimators converge rapidly. We present these results and consider possible consequences for language modeling in general. In particular, other leave-one-out estimators, such as for the cross-entropy of various forms of language models, might also be shown to be rapidly converging using proof methods similar to those used for the Good-Turing estimators. This could have broad ramifications in the analysis and development of language modeling methods. We suggest that, in language modeling at least, leave-one-out estimation may be more significant than Occam's razor.