Techniques for automatically correcting words in text
ACM Computing Surveys (CSUR)
Machine Learning
Computer Programs for Spelling Correction
Computer Programs for Spelling Correction
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
A spelling correction program based on a noisy channel model
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 2
Getting work done on the web: supporting transactional queries
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Navigating the intranet with high precision
Proceedings of the 16th international conference on World Wide Web
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Understanding queries in a search database system
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
SystemT: an algebraic approach to declarative information extraction
ACL '10 Proceedings of the 48th Annual Meeting of the 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
Interactive and context-aware tag spell check and correction
Proceedings of the 21st ACM international conference on Information and knowledge management
Context-aware correction of spelling errors in hungarian medical documents
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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Spelling correction for keyword-search queries is challenging in restricted domains such as personal email (or desktop) search, due to the scarcity of query logs, and due to the specialized nature of the domain. For that task, this paper presents an algorithm that is based on statistics from the corpus data (rather than the query log). This algorithm, which employs a simple graph-based approach, can incorporate different types of data sources with different levels of reliability (e.g., email subject vs. email body), and can handle complex spelling errors like splitting and merging of words. An experimental study shows the superiority of the algorithm over existing alternatives in the email domain.