LT4SG@SMM4H24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models

• URL: http://arxiv.org/abs/2406.07759v1
• Date: Tue, 11 Jun 2024 22:48:18 GMT
• Title: LT4SG@SMM4H24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models
• Authors: Dasun Athukoralage, Thushari Atapattu, Menasha Thilakaratne, Katrina Falkner,
• Abstract summary: This paper presents our approaches for the SMM4H24 Shared Task 5 on the binary classification of English tweets reporting children's medical disorders. Our best-performing system achieves an F1-score of 0.938 on test data, outperforming the benchmark by 1.18%.
• Score: 1.0312118123538199
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents our approaches for the SMM4H24 Shared Task 5 on the binary classification of English tweets reporting children's medical disorders. Our first approach involves fine-tuning a single RoBERTa-large model, while the second approach entails ensembling the results of three fine-tuned BERTweet-large models. We demonstrate that although both approaches exhibit identical performance on validation data, the BERTweet-large ensemble excels on test data. Our best-performing system achieves an F1-score of 0.938 on test data, outperforming the benchmark classifier by 1.18%.

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