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%).- Anthology ID:
- 2023.semeval-1.126
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 913–921
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.126
- DOI:
- 10.18653/v1/2023.semeval-1.126
- Cite (ACL):
- Andra Oica, Daniela Gifu, and Diana Trandabat. 2023. Togedemaru at SemEval-2023 Task 8: Causal Medical Claim Identification and Extraction from Social Media Posts. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 913–921, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Togedemaru at SemEval-2023 Task 8: Causal Medical Claim Identification and Extraction from Social Media Posts (Oica et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/2023.semeval-1.126.pdf