A Winnow-Based Approach to Context-Sensitive Spelling Correction (1999)
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| Venue: | Machine Learning |
| Citations: | 71 - 1 self |
BibTeX
@INPROCEEDINGS{Golding99awinnow-based,
author = {Andrew R. Golding and Dan Roth and J. Mooney and Claire Cardie},
title = {A Winnow-Based Approach to Context-Sensitive Spelling Correction},
booktitle = {Machine Learning},
year = {1999},
pages = {107--130}
}
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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 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, 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 ...
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