Automatic error detection and correction approach in Chinese text based on features and learning

Abstract

Language models adopted by most existing error detection and correction approaches of Chinese text are N-Gram models of characters, words or POS tags. Their deficiencies are that only the local language constraint is employed and there is no language model unification process. A feature-based automatic error detection and correction approach is presented. It uses both local language features and wide-scope semantic features. Winnow is adopted in the learning step. In experiment, this method achieved an error detection recall rate of 85%, precise rate of 41% and error correction rate of 51%. It shows that the approach performs better than the existing approaches based on N-Gram models.