One is how to learn a statistical model to estimate the conditional probability , and the other is how to generate confusion set C of a given query q We take a feature-based approach to model the posterior probability .
we use the maximum entropy model (Berger et al., 1996) for this task: where exp( ) is the normalization factor; , is a feature function defined over query q and correction candidate c , while is the corresponding feature weight.