Learning a Spelling Error Model from Search Query Logs
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.