Applying Winnow to Context-Sensitive Spelling Correction
| dc.creator | Golding, Andrew R. | |
| dc.creator | Roth, Dan | |
| dc.date | 1996-07-19 | |
| dc.date.accessioned | 2026-07-07T09:10:31Z | |
| dc.date.available | 2026-07-07T09:10:31Z | |
| dc.description | 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. | |
| dc.description | 9 pages | |
| dc.identifier | https://arxiv.org/abs/cmp-lg/9607024 | |
| dc.identifier | http://arxiv.org/abs/cmp-lg/9607024 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/151372 | |
| dc.subject | Computation and Language | |
| dc.title | Applying Winnow to Context-Sensitive Spelling Correction | |
| dc.type | text |