Applying Winnow to Context-Sensitive Spelling Correction
<|reference_start|>Applying Winnow to Context-Sensitive Spelling Correction: Multiplicative weight-updating algorithms such as Winnow have been studied extensively in the COLT literature, but only recently have people started to use them in applications. In this paper, we apply a Winnow-based algorithm to a task in natural language: context-sensitive spelling correction. This is the task of fixing spelling errors that happen to result in valid words, such as substituting {\it to\/} for {\it too}, {\it casual\/} for {\it causal}, and so on. Previous approaches to this problem have been statistics-based; we compare Winnow to one of the more successful such approaches, which uses Bayesian classifiers. We find that: (1)~When the standard (heavily-pruned) set of features is used to describe problem instances, Winnow performs comparably to the Bayesian method; (2)~When the full (unpruned) set of features is used, Winnow is able to exploit the new features and convincingly outperform Bayes; and (3)~When a test set is encountered that is dissimilar to the training set, Winnow is better than Bayes at adapting to the unfamiliar test set, using a strategy we will present for combining learning on the training set with unsupervised learning on the (noisy) test set.<|reference_end|>
@article{golding1996applying,
    title={Applying Winnow to Context-Sensitive Spelling Correction},
    author={Andrew R. Golding and Dan Roth},
    journal={arXiv preprint arXiv:cmp-lg/9607024},
    year={1996},
    archivePrefix={arXiv},
    eprint={cmp-lg/9607024},
    primaryClass={cmp-lg cs.CL}
}