Shayona@SMM4H23: COVID-19 Self diagnosis classification using BERT and LightGBM models
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Abstract (arXiv)
This paper describes approaches and results for shared Task 1 and 4 of SMMH4-23 by Team Shayona. Shared Task-1 was binary classification of english tweets self-reporting a COVID-19 diagnosis, and Shared Task-4 was Binary classification of English Reddit posts self-reporting a social anxiety disorder diagnosis. Our team has achieved the highest f1-score 0.94 in Task-1 among all participants. We have leveraged the Transformer model (BERT) in combination with the LightGBM model for both tasks.
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References · 5
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
- 2020COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twittercited by 96
- 2018An Introductory Survey on Attention Mechanisms in NLP Problemscited by 32
- 2020Detection and Classification of mental illnesses on social media using RoBERTacited by 8
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