Discovering Correction Rules for Auto Editing

This paper describes a framework that extracts effective correction rules from a sentence-aligned corpus and shows a practical application: auto-editing using the discovered rules. The framework exploits the methodology of finding the Levenshtein distance between sentences to identify the key parts of the rules and uses the editing corpus to filter, condense, and refine the rules. We have produced the rule candidates of such form, A B, where A stands for the erroneous pattern and B for the correct pattern. The developed framework is language independent; therefore, it can be applied to other languages. The evaluation of the discovered rules reveals that 67.2% of the top 1500 ranked rules are annotated as correct or mostly correct by experts. Based on the rules, we have developed an online auto-editing system for demonstration at http://ppt.cc/02yY.