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 independently-trained 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 Nationa