An Improved Graph Model for Chinese Spell Checking

In this paper, we propose an improved graph model for Chinese spell checking. The model is based on a graph model for generic errors and two independentlytrained models for specific errors. First, a graph model represents a Chinese sentence and a modified single source shortest path algorithm is performed on the graph to detect and correct generic spelling errors. Then, we utilize conditional random fields to solve two specific kinds of common errors: the confusion of “在” (at) (pinyin is ‘zai’ in Chinese), “再” (again, more, then) (pinyin: zai) and “的” (of) (pinyin: de), “地” (-ly, adverb-forming particle) (pinyin: de), “得” (so that, have to) (pinyin: de). Finally, a rule based system is exploited to solve the pronoun usage confusions: “她” (she) (pinyin: ta), “他” (he) (pinyin: ta) and some others fixed collocation errors. The proposed model is evaluated on the standard data set released by the SIGHAN Bake-off 2014 shared task, and gives competitive result. ∗This work was partially supported by the National Natural Science Foundation of China (No. 60903119, No. 61170114, and No. 61272248), the National Basic Research Program of China (No. 2013CB329401), the Science and Technology Commission of Shanghai Municipality (No. 13511500200), the European Union Seventh Framework Program (No. 247619), the Cai Yuanpei Program (CSC fund 201304490199 and 201304490171), and the art and science interdiscipline funds of Shanghai Jiao Tong University (A study on mobilization mechanism and alerting threshold setting for online community, and media image and psychology evaluation: a computational intelligence approach). †Corresponding author.