Query speller is an indispensable part of any modern search engine. In this paper we define the problem of speller performance prediction and apply it to the task of query spelling autocorrection. As candidates for query autocorrection we used the suggestions generated by a query speller. To determine their reliability we used a binary classifier trained on manually labeled data. In addition to the basic standard lexical and statistical features we utilized a number of new click-based features, what allowed to significantly outperform the algorithm trained on the basic set of features.
Noisy channel models, widely used in modern spellers, cope with typical misspellings, but do not work well with infrequent and difficult spelling errors. In this paper, we have improved the noisy channel approach by iterative stochastic search for the best correction. The proposed algorithm allowed us to avoid local minima problem and improve the F 1 measure by 6.6% on distant spelling errors.
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