Paper: Learning A Spelling Error Model From Search Query Logs

ACL ID H05-1120
Title Learning A Spelling Error Model From Search Query Logs
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

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 Expec- tation Maximization algorithm to learn edit distance weights directly from search query logs, without relying on a corpus of paired words.