Pronunciation modeling for improved spelling correction
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
An improved error model for noisy channel spelling correction
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Learning phrase-based spelling error models from clickthrough data
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Fast multi-task learning for query spelling correction
Proceedings of the 21st ACM international conference on Information and knowledge management
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This paper explores the use of online multi-task learning for search query spelling correction, by effectively transferring information from different and biased training datasets for improving spelling correction across datasets. Experiments were conducted on three query spelling correction datasets, including the well-known TREC benchmark data. Our experimental results demonstrate that the proposed method considerably outperforms existing baseline systems in terms of accuracy. Importantly, the proposed method is about one-order of magnitude faster than baseline systems in terms of training speed. In contrast to existing methods which typically require more than (e.g.,) 50 training passes, our algorithm can very closely approach the empirical optimum in around five passes.