Chinese Spelling Errors Detection Based on CSLM

Spelling errors are very common in various electronic documents and it leads to serious influence sometimes. To solve this problem, methods based on the n-gram language model are the most commonly used. CSLM (continuous space language model) which represents a word as a vector is different from traditional models. In this paper, we experimented with a specific CSLM, namely, the CBOW (Continuous Bag-of-Words) model, to detect spelling errors. Since spelling errors are usually considered as wrong characters rather than words in Chinese language, we trained character vectors with a large Chinese corpus, and then judged a Chinese character is right or not by its probability of the occurrence in a given context. Experimental results show that the method based on CSLM outperforms the n-gram language model.