@inproceedings{dahiya-bagga-2024-cogai,
    title = "{C}og{AI}@{SMM}4{H} 2024: Leveraging {BERT}-based Ensemble Models for Classifying Tweets on Developmental Disorders",
    author = "Dahiya, Liza  and
      Bagga, Rachit",
    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/jlcl-multiple-ingestion/2024.smm4h-1.26/",
    pages = "114--116",
    abstract = "This paper presents our work for the Task 5 of the Social Media Mining for Health Applications 2024 Shared Task - Binary classification of English tweets reporting children`s medical disorders. In this paper, we present and compare multiple approaches for automatically classifying tweets from parents based on whether they mention having a child with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), delayed speech, or asthma. We use ensemble of various BERT-based models trained on provided dataset that yields an F1 score of \textbf{0.901} on the test data."
}