Correcting real-word spelling errors by restoring lexical cohesion

Authors:
Graeme Hirst;Alexander Budanitsky
Affiliations:
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 e-mail: gh@cs.toronto.edu, abm@cs.toronto.edu;Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 e-mail: gh@cs.toronto.edu, abm@cs.toronto.edu
Venue:
Natural Language Engineering
Year:
2005

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Abstract

Spelling errors that happen to result in a real word in the lexicon cannot be detected by a conventional spelling checker. We present a method for detecting and correcting many such errors by identifying tokens that are semantically unrelated to their context and are spelling variations of words that would be related to the context. Relatedness to context is determined by a measure of semantic distance initially proposed by Jiang and Conrath (1997). We tested the method on an artificial corpus of errors; it achieved recall of 23–50% and precision of 18–25%.