Chinese Spelling Error Detection Using a Fusion Lattice LSTM

Spelling error detection serves as a crucial preprocessing in many natural language processing applications. Unlike English, where every single word is directly typed by keyboard, we have to use an input method Chinese characters. The pinyin the most widely used. By intuition, should be helpful detecting spelling errors. However, when detect errors, of current methods ignore information and adopt pipeline framework that leads propagation. In this article, propose fusion lattice-LSTM model under end-to-end integrate character, word, features for detection. Experiments on SIGHAN Bake-off-2015 dataset show discriminating feature, our outperforms baseline models obviously.