Conserving Fuel in Statistical Language Learning: Predicting Data Requirements
<|reference_start|>Conserving Fuel in Statistical Language Learning: Predicting Data Requirements: In this paper I address the practical concern of predicting how much training data is sufficient for a statistical language learning system. First, I briefly review earlier results and show how these can be combined to bound the expected accuracy of a mode-based learner as a function of the volume of training data. I then develop a more accurate estimate of the expected accuracy function under the assumption that inputs are uniformly distributed. Since this estimate is expensive to compute, I also give a close but cheaply computable approximation to it. Finally, I report on a series of simulations exploring the effects of inputs that are not uniformly distributed. Although these results are based on simplistic assumptions, they are a tentative step toward a useful theory of data requirements for SLL systems.<|reference_end|>
@article{lauer1995conserving,
    title={Conserving Fuel in Statistical Language Learning: Predicting Data
  Requirements},
    author={Mark Lauer (Microsoft Institute, Sydney)},
    journal={Eighth Australian Joint Conference on Artificial Intelligence,
  Canberra, 1995.},
    year={1995},
    archivePrefix={arXiv},
    eprint={cmp-lg/9509002},
    primaryClass={cmp-lg cs.CL}
}