Applying the noisy channel model to search query spelling correction requires an error model and a language model. Typically, the error model relies on a weighted string edit distance measure. The weights can be learned from pairs of misspelled words and their corrections. This paper investigates using the Expectation Maximization algorithm to learn edit distance weights directly from search query logs, without relying on a corpus of paired words.
REF propose a method of estimating an error model from query logs using the EM algorithm.
Learning A Spelling Error Model From Search Query Logs
Human Language Technology Conference And Empirical Methods In Natural Language Processing
Computer Science