@inproceedings{fu-etal-2022-casia-smm4h22,
title = "{CASIA}@{SMM}4{H}{'}22: A Uniform Health Information Mining System for Multilingual Social Media Texts",
author = "Fu, Jia and
Li, Sirui and
Yuan, Hui Ming and
Li, Zhucong and
Gan, Zhen and
Chen, Yubo and
Liu, Kang and
Zhao, Jun and
Liu, Shengping",
editor = "Gonzalez-Hernandez, Graciela and
Weissenbacher, Davy",
booktitle = "Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.smm4h-1.39",
pages = "143--147",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T CASIA@SMM4H’22: A Uniform Health Information Mining System for Multilingual Social Media Texts
%A Fu, Jia
%A Li, Sirui
%A Yuan, Hui Ming
%A Li, Zhucong
%A Gan, Zhen
%A Chen, Yubo
%A Liu, Kang
%A Zhao, Jun
%A Liu, Shengping
%Y Gonzalez-Hernandez, Graciela
%Y Weissenbacher, Davy
%S Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F fu-etal-2022-casia-smm4h22
%X 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.
%U https://aclanthology.org/2022.smm4h-1.39
%P 143-147
Markdown (Informal)
[CASIA@SMM4H’22: A Uniform Health Information Mining System for Multilingual Social Media Texts](https://aclanthology.org/2022.smm4h-1.39) (Fu et al., SMM4H 2022)
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.