http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
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- 주제어 : 深度学习 (검색결과 2 건)
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深度学习支持下的韩国专门领域汉语文本分类研究 ——以能源动力与材料领域为例
肖锐,张邝弋 중한연구학회 2023 중한연구학간 Vol.0 No.32
Objective: The progress of artificial intelligence automation technology is of great significance for promoting the implementation of the “Chinese+vocational skills”strategy. To fully promote research on specialized Chinese language teaching in South Korea, and to enable adaptive technology to be applied more efficiently and on a large scale, this study proposes a text classification method for energy and materials based on deep learning. This study uses Recurrent Neural Networks (RNN), Long Short Term Memory Networks (LSTM), and Bidirectional Long Short Term Memory Networks (Bi LSTM) to classify 660000-word corpora from seven categories of energy power and materials, including power technology, thermal energy, and power generation engineering, new energy power generation engineering, ferrous metal materials, non-ferrous metal materials, non-metallic materials, and building materials. The experimental results show that the text classification model based on deep learning performs better overall than traditional machine learning algorithms in the task of Chinese energy power and materials professional text classification. Among them, the RNN model performs the best, achieving an accuracy of 94.95%, a recall rate of 96.33%, and an F1 value of 94.91%. This proves the effectiveness and superiority of the RNN model in processing Chinese energy power and materials professional text, This has laid a solid foundation for the further construction of a Chinese vocabulary for energy, power, and materials majors, and provided a research paradigm for the construction of a specialized Chinese teaching vocabulary in South Korea, promoting the continuous improvement of the internationalization level and inf luence of specialized Chinese teaching.
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