@inproceedings{afonso-etal-2024-bit,
    title = "{BIT}@{UA} at {\#}{SMM}4{H} 2024 Tasks 1 and 5: finding adverse drug events and children{'}s medical disorders in {E}nglish tweets",
    author = "Afonso, Luis  and
      Almeida, Jo{\~a}o  and
      Antunes, Rui  and
      Oliveira, Jos{\'e}",
    editor = "Xu, Dongfang  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.smm4h-1.37/",
    pages = "158--162",
    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."
}