Context based spelling correction
Information Processing and Management: an International Journal
Spelling correction for the telecommunications network for the deaf
Communications of the ACM
Techniques for automatically correcting words in text
ACM Computing Surveys (CSUR)
The weighted majority algorithm
Information and Computation
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
Combining trigram and Winnow in thai OCR error correction
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Japanese OCR error correction using character shape similarity and statistical language model
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Part of speech tagging using a network of linear separators
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Recognizing syntactic errors in the writing of second language learners
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Combining Trigram-based and feature-based methods for context-sensitive spelling correction
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Combining Methods for Detecting and Correcting Semantic Hidden Errors in Arabic Texts
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Hi-index | 0.00 |
The researches on spelling correction aimmg at detecting errors in texts tend context-sensitive spellng error correction, which is more difficult than traditional isloated-word error correction. A novel and efficient algorithm for the system of Chinese spelling error correction, CInsunSpell, is presented. In this system, the work of correction lncludes two parts: checking phase and correcting phase. At the first phase, a Trigram algorithm within one fixed-size window is designed to locate potenuat errors in local area. This second phase employs a new method ot automatically and dynamically distributing weights among the characters in the confusion set as well as in the Bayesian language model. The tactics used abov exhibits good performances.