Chinese Grammatical Error Detection Using a CNN-LSTM Model

Authors

  • Lung-Hao LEE Graduate Institute of Library and Information Studies, National Taiwan Normal University, Taiwan Author
  • Bo-Lin LIN Department of Information Management, Yuan Ze University, Taiwan; Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taiwan Author
  • Liang-Chih YU Department of Information Management, Yuan Ze University, Taiwan; Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taiwan Author
  • Yuen-Hsien TSENG Graduate Institute of Library and Information Studies, National Taiwan Normal University, Taiwan Author

Abstract

In this paper, we proposed a Convolution Neural Network with Long Short-Term Memory (CNN-LSTM) model for Chinese grammatical error detection. The TOCFL learner corpus is adopted to measure the system performance of indicating whether a sentence contains errors or not. Our model performs better than other neural network based methods in terms of accuracy for identifying an erroneous sentence written by Chinese language learners.

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Published

2017-12-04

Conference Proceedings Volume

2017: ICCE 2017: The 25th International Conference on Computers in Education

Section

Articles

How to Cite

Chinese Grammatical Error Detection Using a CNN-LSTM Model. (2017). International Conference on Computers in Education. https://library.apsce.net/index.php/ICCE/article/view/2198