Abstract
Chinese spelling check is an important component for many NLP applications, including word processor and search engines. However, compared to checkers for alphabetical languages (e.g., English or French), Chinese spelling checkers are more difficult to develop, because there are no word boundaries in Chinese writing system, and errors may be caused by various Chinese input methods. In this paper, we proposed a novel method to Chinese spelling checking. Our approach involves error detection and correction based on the phrasal statistical machine translation framework. The results show that the proposed system achieves significantly better accuracy in error detecting and more satisfactory performance in error correcting.