@inproceedings{unnikrishnan-etal-2022-halelab,
    title = "{H}ale{L}ab{\_}{NITK}@{SMM}4{H}{'}22: Adaptive Learning Model for Effective Detection, Extraction and Normalization of Adverse Drug Events from Social Media Data",
    author = "Unnikrishnan, Reshma  and
      Kamath S, Sowmya  and
      V. S., Ananthanarayana",
    editor = "Gonzalez-Hernandez, Graciela  and
      Weissenbacher, Davy",
    booktitle = "Proceedings of the Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.smm4h-1.27/",
    pages = "95--97",
    abstract = "This paper describes the techniques designed for detecting, extracting and normalizing adverse events from social data as part of the submission for the Shared task, Task 1-SMM4H{'}22. We present an adaptive learner mechanism for the foundation model to identify Adverse Drug Event (ADE) tweets. For the detected ADE tweets, a pipeline consisting of a pre-trained question-answering model followed by a fuzzy matching algorithm was leveraged for the span extraction and normalization tasks. The proposed method performed well at detecting ADE tweets, scoring an above-average F1 of 0.567 and 0.172 overlapping F1 for ADE normalization. The model{'}s performance for the ADE extraction task was lower, with an overlapping F1 of 0.435."
}