CASIA @ SMM 4 H ’22: A Uniform Health Information Mining System for Multilingual Social Media Texts (2022)

Abstract: 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.

## introduction a large amount of health-related data exists on social media, and this data has attracted a lot of attention in medical applications. the social media mining for health applications 2022 (smm4h-2022) shared task(davy weissenbacher, 2022) involves natural language processing (nlp) challenges of using social media data for health research, including informal, colloquial expressions and misspellings of clinical concepts, noise, data sparsity, ambiguity, and multilingual posts. we participated in seven english classification tasks (i.e., task 1a, 4, 6, 7, 8, 9) and one spanish named entity † first author and second author contribute equally to this work. there are three challenges in the smm4h 2022 shared tasks. the first one is the domain shift problem. when fine-tuning specific downstream tasks using large-scale pre-trained models, the increasing size of the pre-trained models leads to the domain bias problem. to address this problem, we apply continue pre-training @xcite to enhance the adaptive ability of the model in the corresponding domain. also, we incorporate fgm adversarial training and child-tuning @xcite to improve the robustness of the model. the second challenge is the prediction bias due to category imbalance. the category imbalance in the annotated data affects the focus of the model learning, which eventually leads to prediction bias and also hinders the robustness and generalization of the model. therefore, we apply oversampling to generate samples from the minority class to achieve data balance. the third challenge is the noise in informal text. when learning from informal social media text data, the text contains a large number of emojis, mentioned usernames, and hyperlinks, which can affect the model's ability to learn the true semantic information in the text. thus, we apply data cleaning to remove the noise from the data. therefore, addressing the three challenges, the three approaches of this paper can be summarized as below: ## conclusion and future work the above tasks in the smm4h 2022 explore social perceptions of health and diseases using social media data, including classification and information extraction tasks. in this paper, we propose a unified system to solve the classification and named entity recognition tasks, which can be applied to both english and spanish scenarios. our framework consists of data cleaning, oversampling, continued pre-training, and applying adversarial training and child-tuning for fine-tuning. we evaluated different variants of our framework and demonstrated the effectiveness of the framework. for future work, we will introduce knowledge graphs from the medical field to further improve our system.