Adaptive Context-Sensitive Spelling Error Correction Techniques for the Extremely Unpredictable Error Generating Language Environments

This research, which considered context-sensitive spelling error correction as the classification problem of words along with the context as the solution of semantic ambiguity, has a limitation that correction can be corrected in a particular words pair. To overcome this, this research suggested a technique for detecting and correcting context-sensitive spelling error probabilistically, by selecting the whole eojeol1 as the target words and generating corresponding available candidate. The context-sensitive spelling error correction model suggested by this research is a model based on the noisy channel. It utilized eojeol n-gram model as a language model, and the context-sensitive spelling error rate, the channel probability, was utilized as an equipment for user adaptive context-sensitive spelling error detection and correction. This research composed an evaluation data by transforming eojeol per sentence from 2,000 correct sentences randomly extracted from Sejong corpus to error eojeol. The test result showed function, i.e. precision over 95%, and recall over 70%. This showed the detection and correction method suggested by this research could be applied in the extremely unpredictable error generating language environments.