@inproceedings{bansal-etal-2026-halelab,
    title = "{HALEL}ab-{NITK} at {\#}{SMM}4{H}-{H}ea{RD}2026: Inclusion of Feature Engineering for Detection of Patient Metadata in {SARS}-{C}o{V}2 Sequencing Articles",
    author = "Bansal, Aakarsh  and
      Srinivas, Abhishek  and
      Kamath S., Sowmya",
    editor = "Lopez-Garcia, Guillermo  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the 11th Social Media Mining for Health Research and Applications ({SMM}4{H}-{H}ea{RD} 2026) Workshop and Shared Tasks",
    month = jul,
    year = "2026",
    address = "San Diego, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl-awards/2026.smm4h-1.35/",
    doi = "10.18653/v1/2026.smm4h-1.35",
    pages = "222--224",
    ISBN = "979-8-89176-432-3",
    abstract = "This article presents a system description for our work as part of Task 5 of the SMM4H-HeaRD 2026 workshop. We fine-tune pretrained BERT and BiomedBERT models and further enhance them using custom feature augmentation techniques. Incorporating these engineered features results in improved performance, with the best model achieving a validation F1 score of 0.8419 and an evaluation phase F1 score of 0.753."
}