A Spelling Correction Program Based on a Noisy Channel Model

This paper describes a new program, correct, which takes words rejected by the Unix spell program, proposes a list of candidate corrections, and sorts them by probability. The probability scores are the novel contribution of this work. Probabilities are based on a noisy channel model. It is assumed that the typist knows what words he or she wants to type but some noise is added on the way to the keyboard (in the form of typos and spelling errors). Using a classic Bayesian argument of the kind that is very popular in the speech recognition literature (Jelinek, 1985), one can often recover the intended correction, c, from a typo, t, by finding the correction c that maximizes Pr(c) Pr(tc). The first factor, Pr(c), is a prior model of word probabilities; the second factor, Pr(tc), is a model of the noisy channel that accounts for spelling transformations on letter sequences (e.g., insertions, deletions, substitutions and reversals). Both sets of probabilities were trained on data collected from the Associated Press (AP) newswire. This text is ideally suited for this purpose since it contains a large number of typos (about two thousand per month).