Abstract
In this paper we present our proposed systems, for Tasks 1 and 5 of the #SMM4H-2024 shared task (Social Media Mining for Health), responsible for identifying health-related aspects in English social media text. Task 1 consisted of identifying text spans mentioning adverse drug events and linking them to unique identifiers from the medical terminology MedDRA, whereas in Task 5 the aim was to distinguish tweets that report a user having a child with a medical disorder from tweets that merely mention a disorder.For Task 1, our system, composed of a pre-trained RoBERTa model and a random forest classifier, achieved 0.397 and 0.295 entity recognition and normalization F1-scores respectively. In Task 5, we obtained a 0.840 F1-score using a pre-trained BERT model.- Anthology ID:
- 2024.smm4h-1.37
- Volume:
- Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Dongfang Xu, Graciela Gonzalez-Hernandez
- Venues:
- SMM4H | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 158–162
- Language:
- URL:
- https://aclanthology.org/2024.smm4h-1.37
- DOI:
- Cite (ACL):
- Luis Afonso, João Almeida, Rui Antunes, and José Oliveira. 2024. BIT@UA at #SMM4H 2024 Tasks 1 and 5: finding adverse drug events and children’s medical disorders in English tweets. In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, pages 158–162, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- BIT@UA at #SMM4H 2024 Tasks 1 and 5: finding adverse drug events and children’s medical disorders in English tweets (Afonso et al., SMM4H-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.smm4h-1.37.pdf