Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Techniques for automatically correcting words in text
ACM Computing Surveys (CSUR)
Machine Learning - Special issue on inductive transfer
On-line learning and stochastic approximations
On-line learning in neural networks
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Automatic Rule Acquisition for Spelling Correction
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A spelling correction program based on a noisy channel model
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 2
Learning to learn with the informative vector machine
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Pronunciation modeling for improved spelling correction
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Learning Gaussian processes from multiple tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Exploring distributional similarity based models for query spelling correction
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Learning a spelling error model from search query logs
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
The matrix stick-breaking process for flexible multi-task learning
Proceedings of the 24th international conference on Machine learning
Using the web for language independent spellchecking and autocorrection
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Multitask learning with expert advice
COLT'07 Proceedings of the 20th annual conference on Learning theory
Learning phrase-based spelling error models from clickthrough data
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Online learning for multi-task feature selection
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A large scale ranker-based system for search query spelling correction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Online spelling correction for query completion
Proceedings of the 20th international conference on World wide web
Theory and applications of b-bit minwise hashing
Communications of the ACM
Character confusion versus focus word-based correction of spelling and OCR variants in corpora
International Journal on Document Analysis and Recognition - Special issue on noisy text analytics
A fast and accurate method for approximate string search
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
COLT'06 Proceedings of the 19th annual conference on Learning Theory
A New Multi-task Learning Method for Personalized Activity Recognition
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Query spelling correction using multi-task learning
Proceedings of the 21st international conference companion on World Wide Web
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In this paper, we explore the use of a novel online multi-task learning framework for the task of search query spelling correction. In our procedure, correction candidates are initially generated by a ranker-based system and then re-ranked by our multi-task learning algorithm. With the proposed multi-task learning method, we are able to effectively transfer information from different and highly biased training datasets, for improving spelling correction on all datasets. Our experiments are conducted on three query spelling correction datasets including the well-known TREC benchmark dataset. The experimental results demonstrate that our proposed method considerably outperforms the 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. Compared to the commonly used online learning methods which typically require more than (e.g.,) 60 training passes, our proposed method is able to closely reach the empirical optimum in about 5 passes.