Yong-hun Lee, JeeHee Yu, and Tae-Jin Yoon. 2017. Predicting the Occurrence of the English Modals Can and May Using Deep Neural Networks. Studies in Modern Grammar 96, 167-189. This paper tries to provide a computational modeling of language processing using deep neural networks. For this purpose, the corpus data in the ICE-USA was used. After all the sentences with can and may were encoded with eighteen linguistic factors, the annotated data were fed into the deep neural networks (DNN). The DNN was constructed with three layers, and each layer contained seventeen nodes. After the DNN was constructed, the learning process was performed with a training set. Then, the performance was measured with a test set. The processes were repeated one hundred times, and it was observed that the DNN had the classification accuracy of 91.5%. The results are promising in that reliable methods can be used in automatically classifying the frequently used modal auxiliary on the basis of the deep learning system.

1. Introduction
 2. Previous Studies
  2.1. Previous Studies on Can and May
  2.2. Machine Learning, Deep Learning, and Neural Networks
 3. Language Modeling Using Deep Neural Networks
  3.1. Neural Networks and Language Modeling
  3.2. Corpus data
  3.3. Steps towards Deep Neural Networks
 4. Testing the DNN Model
  4.1. Testing Method
  4.2. Analysis results
 5. Discussions
 6. Conclusion
 References

• Yong-hun Lee(Chungnam National University)
• JeeHee Yu(Hannam University)
• Tae-Jin Yoon(Sungshin Women’s University) Corresponding author