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


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.
Anthology ID:
2022.smm4h-1.39
Volume:
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
SMM4H
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
143–147
Language:
URL:
https://aclanthology.org/2022.smm4h-1.39
DOI:
Bibkey:
Cite (ACL):
Jia Fu, Sirui Li, Hui Ming Yuan, Zhucong Li, Zhen Gan, Yubo Chen, Kang Liu, Jun Zhao, and Shengping Liu. 2022. CASIA@SMM4H’22: A Uniform Health Information Mining System for Multilingual Social Media Texts. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 143–147, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
CASIA@SMM4H’22: A Uniform Health Information Mining System for Multilingual Social Media Texts (Fu et al., SMM4H 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.smm4h-1.39.pdf