A Machine Learning Model for the Dating of Ancient Chinese Texts
Xuejin Yu, Wei Huangfu · 2019
This paper, with the intent of solving the issues on the dating of ancient Chinese texts, takes advantage of the Long-Short Term Memory Network (LSTM) to analyze and process the character sequence in ancient Chinese. In this model, each character is transformed into a high-dimensional vector, and then vectors and the non-linear relationships among them are read and analyzed by LSTM, which finally achieve the dating tags. Experimental results show that the LSTM has a strong ability to date the ancient texts, and the precision reaches about 95% in our experiments. Thus, the proposed model offers an effective method on how to date the ancient Chinese texts. It also inspires us to actively improve the time-consuming analysis tasks in the Chinese NLP field.