Togedemaru at SemEval-2023 Task 8: Causal Medical Claim Identification and Extraction from Social Media Posts
Andra Oica, Daniela Gifu, Diana Trandabat
The 17th International Workshop on Semantic Evaluation (SemEval-2023) Task 8: causal medical claim identification and related pico frame extraction from social media posts Paper
TLDR:
The "Causal Medical Claim Identification and Extraction from Social Media Posts task at SemEval 2023 competition focuses on identifying and validating medical claims in English, by posing two subtasks on causal claim identification and PIO (Population, Intervention, Outcome) frame extraction. In the
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Abstract:
The "Causal Medical Claim Identification and Extraction from Social Media Posts task at SemEval 2023 competition focuses on identifying and validating medical claims in English, by posing two subtasks on causal claim identification and PIO (Population, Intervention, Outcome) frame extraction. In the context of SemEval, we present a method for sentence classification in four categories (claim, experience, experience\_based\_claim or a question) based on BioBERT model with a MLP layer. The website from which the dataset was gathered, Reddit, is a social news and content discussion site. The evaluation results show the effectiveness of the solution of this study (83.68\%).