Context based spelling correction
Information Processing and Management: an International Journal
Automatic spelling correction in scientific and scholarly text
Communications of the ACM
Computer programs for detecting and correcting spelling errors
Communications of the ACM
A technique for computer detection and correction of spelling errors
Communications of the ACM
Machine Learning
Automatic Rule Acquisition for Spelling Correction
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
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
An improved error model for noisy channel spelling correction
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Minimal commitment and full lexical disambiguation: balancing rules and hidden Markov Models
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Hi-index | 0.00 |
We report on the design of a system for correcting spelling errors resulting in non-existent words. The system aims at improving edition of medical reports. Unlike traditional systems, both semantic and syntactic contexts are considered here. The system is organized along three steps. The first module is based on a context independent string-to-string edit distance calculus. The second module, based on the morpho-syntactic context attempts to rank more relevantly the data set provided by the first module, finally a third contextual module processes words with the same part-of-speech by applying some contextual word-sense disambiguation. Modules 2 and 3 are using both hand written rules and data-driven Markovian matrices. A final evaluation shows a significant improvement compared to context-free spelling correction.