  Spelling correction is one of the main tasks in the field of Natural Language
Processing. Contrary to common spelling errors, real-word errors cannot be
detected by conventional spelling correction methods. The real-word correction
model proposed by Mays, Damerau and Mercer showed a great performance in
different evaluations. In this research, however, a new hybrid approach is
proposed which relies on statistical and syntactic knowledge to detect and
correct real-word errors. In this model, Constraint Grammar (CG) is used to
discriminate among sets of correction candidates in the search space. Mays,
Damerau and Mercer's trigram approach is manipulated to estimate the
probability of syntactically well-formed correction candidates. The approach
proposed here is tested on the Wall Street Journal corpus. The model can prove
to be more practical than some other models, such as WordNet-based method of
Hirst and Budanitsky and fixed windows size method of Wilcox-O'Hearn and Hirst.
