Combining Trigram-based and Feature-based Methods for Context-Sensitive Spelling Correction

Combining Trigram-based and Feature-based Methods for Context-Sensitive Spelling Correction (1996)

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by Andrew R. Golding , Yves Schabes

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Datum	Value	Source
TITLE	Combining Trigram-based and Feature-based Methods for Context-Sensitive Spelling Correction	SVM HeaderParse 0.1
AUTHOR NAME	Andrew R. Golding	user correction - Legacy Corrections
AUTHOR NAME	Yves Schabes	user correction - Legacy Corrections
ABSTRACT	This paper addresses the problem of correcting spelling errors that result in valid, though unintended words (such as peace and piece, or quiet and quite) and also the problem of correcting particular word usage errors (such as amount and number, or among and between). Such cor- rections require contextual information and are not handled by conventional spelling programs such as Unix spell. First, we introduce a method called Trigrams that uses part-of-speech trigrams to encode the context. This method uses a small num- ber of parameters compared to previous methods based on word trigrams. However, it is effectively unable to distinguish among words that have the same part of speech. For this case, an alternative feature-based method called Bayes performs better; but Bayes is less effective than Trigrams when the distinction among words depends on syntactic constraints. A hybrid method called Tribayes is then introduced that combines the best of the previous two methods. The improvement in performance of Tribayes over its components is verified experimentally. Tribayes is also compared with the grammar checker in Microsoft Word, and is found to have substantially higher performance.	user correction - Legacy Corrections
YEAR	1996	user correction - Legacy Corrections
CITATIONS	10 found	ParsCit 1.0