Research on Identification of COVID-19 News Elements based on Transfer Learning in Multi-task Environment - Zhao Zibo; Wang Hao; Liu Youhua; Zhang Wei; Meng Zhen (Zhishi guanli luntan, 2021)
[Purpose/significance] Under the background of novel coronavirus pneumonia, this paper proposes a method of identifying COVID-19 news elements in multi-task environment based on transfer learning to provide knowledge services of emergency for the public. [Method/process] Firstly, multiple tasks were used to identify news elements: Time elements were identified based on rules; besides, a cross domain element recognition model was constructed by integrating model transfer and deep learning methods. On this basis, the associated data of COVID-19 news elements was constructed, and the relationship between the elements was displayed by knowledge mapping. [Result/conclusion] The experimental results show that the F1 values of news elements except Drug are above 80%, which indicates that the transfer learning model can achieve fine recognition effect. Moreover, the knowledge map of associated data can intuitively display the key elements and main contents of news. In conclusion, the method proposed in this paper can effectively identify elements in COVID-19 news, thus it can help readers obtain important information from the news accurately and efficiently.