Language models for contextual error detection and correction

Stehouwer, H., & van Zaanen, M. (2009). Language models for contextual error detection and correction. In Proceedings of the EACL 2009 Workshop on Computational Linguistic Aspects of Grammatical Inference (pp. 41-48). Association for Computational Linguistics.
The problem of identifying and correcting confusibles, i.e. context-sensitive spelling errors, in text is typically tackled using specifically trained machine learning classifiers. For each different set of confusibles, a specific classifier is trained and tuned. In this research, we investigate a more generic approach to context-sensitive confusible correction. Instead of using specific classifiers, we use one generic classifier based on a language model. This measures the likelihood of sentences with different possible solutions of a confusible in place. The advantage of this approach is that all confusible sets are handled by a single model. Preliminary results show that the performance of the generic classifier approach is only slightly worse that that of the specific classifier approach
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