  A large class of machine-learning problems in natural language require the
characterization of linguistic context. Two characteristic properties of such
problems are that their feature space is of very high dimensionality, and their
target concepts refer to only a small subset of the features in the space.
Under such conditions, multiplicative weight-update algorithms such as Winnow
have been shown to have exceptionally good theoretical properties. We present
an algorithm combining variants of Winnow and weighted-majority voting, and
apply it to a problem in the aforementioned class: context-sensitive spelling
correction. This is the task of fixing spelling errors that happen to result in
valid words, such as substituting "to" for "too", "casual" for "causal", etc.
We evaluate our algorithm, WinSpell, by comparing it against BaySpell, a
statistics-based method representing the state of the art for this task. We
find: (1) When run with a full (unpruned) set of features, WinSpell achieves
accuracies significantly higher than BaySpell was able to achieve in either the
pruned or unpruned condition; (2) When compared with other systems in the
literature, WinSpell exhibits the highest performance; (3) The primary reason
that WinSpell outperforms BaySpell is that WinSpell learns a better linear
separator; (4) When run on a test set drawn from a different corpus than the
training set was drawn from, WinSpell is better able than BaySpell to adapt,
using a strategy we will present that combines supervised learning on the
training set with unsupervised learning on the (noisy) test set.
