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In order to detect Chinese spelling errors, especially for essays written by foreign learners, a word vector/conditional random field (CRF)based detector is proposed in this paper. The main idea is to project each word in a test sentence into a high dimensional vector space in order to reveal and examine their relationships by using a CRF. The results are then utilized to constrain the time-consuming language model rescoring procedure. Official SIGHAN2015 evaluation results show that our system did achieve reasonable performance with about 0.601/0.564 ac-curacies and 0.457/0.375 F1 scores in the detection/correction levels.

Word Vector/Conditional Random Field-based Chinese Spelling Error Detection for SIGHAN-2015 Evaluation