@inproceedings{lin-etal-2022-ncuee,
title = "{NCUEE}-{NLP}@{SMM}4{H}{'}22: Classification of Self-reported Chronic Stress on {T}witter Using Ensemble Pre-trained Transformer Models",
author = "Lin, Tzu-Mi and
Chen, Chao-Yi and
Tzeng, Yu-Wen and
Lee, Lung-Hao",
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-1.18",
pages = "62--64",
abstract = "This study describes our proposed system design for the SMM4H 2022 Task 8. We fine-tune the BERT, RoBERTa, ALBERT, XLNet and ELECTRA transformers and their connecting classifiers. Each transformer model is regarded as a standalone method to detect tweets that self-reported chronic stress. The final output classification result is then combined using the majority voting ensemble mechanism. Experimental results indicate that our approach achieved a best F1-score of 0.73 over the positive class.",
}
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%0 Conference Proceedings
%T NCUEE-NLP@SMM4H’22: Classification of Self-reported Chronic Stress on Twitter Using Ensemble Pre-trained Transformer Models
%A Lin, Tzu-Mi
%A Chen, Chao-Yi
%A Tzeng, Yu-Wen
%A Lee, Lung-Hao
%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 lin-etal-2022-ncuee
%X This study describes our proposed system design for the SMM4H 2022 Task 8. We fine-tune the BERT, RoBERTa, ALBERT, XLNet and ELECTRA transformers and their connecting classifiers. Each transformer model is regarded as a standalone method to detect tweets that self-reported chronic stress. The final output classification result is then combined using the majority voting ensemble mechanism. Experimental results indicate that our approach achieved a best F1-score of 0.73 over the positive class.
%U https://aclanthology.org/2022.smm4h-1.18
%P 62-64
Markdown (Informal)
[NCUEE-NLP@SMM4H’22: Classification of Self-reported Chronic Stress on Twitter Using Ensemble Pre-trained Transformer Models](https://aclanthology.org/2022.smm4h-1.18) (Lin et al., SMM4H 2022)
ACL