LT4SG@SMM4H24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909222186254336 |
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| author | Athukoralage, Dasun Atapattu, Thushari Thilakaratne, Menasha Falkner, Katrina |
| author_facet | Athukoralage, Dasun Atapattu, Thushari Thilakaratne, Menasha Falkner, Katrina |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07759 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LT4SG@SMM4H24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models Athukoralage, Dasun Atapattu, Thushari Thilakaratne, Menasha Falkner, Katrina Computation and Language 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%. |
| title | LT4SG@SMM4H24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.07759 |