Abstract
This paper describes our system in the Chinese Grammatical Error Diagnosis (CGED) task for learning Chinese as a Foreign Language (CFL). Our work adopts a hybrid model by integrating rulebased method and n-gram statistical method to detect Chinese grammatical errors, identify the error type and point out the position of error in the input sentences. Tri-gram is applied to disorder mistake. And the rest of mistakes are solved by the conservation rules sets. Empirical evaluation results demonstrate the utility of our CGED system.
Cite
CITATION STYLE
Wu, X., Huang, P., Wang, J., Guo, Q., Xu, Y., & Chen, C. (2015). Chinese Grammatical Error Diagnosis System Based on Hybrid Model. In Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications, NLP-TEA 2015 - in conjunction with the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2015 (pp. 117–125). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w15-4418
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