  Spelling correction is a fundamental task in Text Mining. In this study, we
assess the real-word error correction model proposed by Mays, Damerau and
Mercer and describe several drawbacks of the model. We propose a new variation
which focuses on detecting and correcting multiple real-word errors in a
sentence, by manipulating a Probabilistic Context-Free Grammar (PCFG) to
discriminate between items in the search space. We test our approach on the
Wall Street Journal corpus and show that it outperforms Hirst and Budanitsky's
WordNet-based method and Wilcox-O'Hearn, Hirst, and Budanitsky's fixed windows
size method.-O'Hearn, Hirst, and Budanitsky's fixed windows size method.
