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Abstract

Abstract.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 depend on 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. In the work reported here, we present an algorithm combining variants of Winnow and weightedmajority 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, and so on. 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) While several aspects of WinSpell's architecture contribute to its superiority over BaySpell, the primary factor is that it is able to learn a better linear separator than BaySpell learns; (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.

Survey of Automatic Spelling Correction

“…Another form of a statistical classifier for the context modeling with multiple models is the Winnow algorithm [96,103]. This approach uses several Winnow classifiers trained with different parameters.…”

“…The model uses the same features (occurrence of a word in context and collocation of tags and surrounding word) as those in the previous approach [67]. The paper by Golding and Roth [96] was followed by Carlson et al [97], which used a large-scale training corpus. Also, Li and Wang [95] proposed a similar system for Chinese spelling correction.…”