Spelling check for Chinese has more challenging difficulties than that for other languages. A hybrid model for Chinese spelling check is presented in this article. The hybrid model consists of three components: one graph-based model for generic errors and two independently trained models for specific errors. In the graph model, a directed acyclic graph is generated for each sentence, and the single-source shortest-path algorithm is performed on the graph to detect and correct general spelling errors at the same time. Prior to that, two types of errors over functional words (characters) are first solved by conditional random fields: 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 ), and “得” ( so that , have to ) (pinyin: de ). Finally, a rule-based model is exploited to distinguish pronoun usage confusion: “她” ( she ) (pinyin: ta ), “他” ( he ) (pinyin: ta ), and some other common collocation errors. The proposed model is evaluated on the standard datasets released by the SIGHAN Bake-off shared tasks, giving state-of-the-art results.

It is very import for Chinese language pro-cessing with the aid of an efficient input method engine (IME), of which pinyin-to-Chinese (PTC) conversion is the core part. Meanwhile, though typos are in-evitable during user pinyin inputting, ex-isting IMEs paid little attention to such big inconvenience. In this paper, motivated by a key equivalence of two decoding algo-rithms, we propose a joint graph model to globally optimize PTC and typo correction for IME. The evaluation results show that the proposed method outperforms both ex-isting academic and commercial IMEs.