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
Publication type
Proceedings paper
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