CASIA@SMM4H’22: A Uniform Health Information Mining System for Multilingual Social Media Texts
Jia Fu, Sirui Li, Hui Ming Yuan, Zhucong Li, Zhen Gan, Yubo Chen, Kang Liu, Jun Zhao, Shengping Liu
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This paper presents a description of our system in SMM4H-2022, where we participated in task 1a,task 4, and task 6 to task 10. There are three main challenges in SMM4H-2022, namely the domain shift problem, the prediction bias due to category imbalance, and the noise in informal text. In this paper, we propose a unified framework for the classification and named entity recognition tasks to solve the challenges, and it can be applied to both English and Spanish scenarios. The results of our system are higher than the median F1-scores for 7 tasks and significantly exceed the F1-scores for 6 tasks. The experimental results demonstrate the effectiveness of our system.