Exploiting Syntactic and Distributional Information for Spelling Correction with Web-Scale N-gram Models

We propose a novel way of incorporating dependency parse and word co-occurrence information into a state-of-the-art web-scale ngram model for spelling correction. The syntactic and distributional information provides extra evidence in addition to that provided by a web-scale n-gram corpus and especially helps with data sparsity problems. Experimental results show that introducing syntactic features into n-gram based models significantly reduces errors by up to 12.4% over the current state-of-the-art. The word co-occurrence information shows potential but only improves overall accuracy slightly.