@inproceedings{babasaheb-madasamy-2025-scaler,
    title = "{SC}a{LER}@{ALTA} 2025: Hybrid and Bi-Encoder Approaches for Adverse Drug Event Mention Normalization",
    author = "Babasaheb, Shelke Akshay  and
      Madasamy, Anand Kumar",
    editor = "Kummerfeld, Jonathan K.  and
      Joshi, Aditya  and
      Dras, Mark",
    booktitle = "Proceedings of The 23rd Annual Workshop of the Australasian Language Technology Association",
    month = nov,
    year = "2025",
    address = "Sydney, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-alta/2025.alta-main.17/",
    pages = "224--229",
    ISBN = "1834-7037",
    abstract = "This paper describes the system developed by Team Scaler for the ALTA 2025 Shared Task on Adverse Drug Event (ADE) Mention Normalization. The task aims to normalize freetext mentions of adverse events to standardized MedDRA concepts. We present and compare two architectures: (1) a Hybrid Candidate Generation + Neural Reranker approach using a pretrained PubMedBERT model, and (2) a BiEncoder model based on SapBERT, fine-tuned to align ADE mentions with MedDRA concepts. The hybrid approach retrieves candidate terms through semantic similarity search and refines the ranking using a neural reranker, while the bi-encoder jointly embeds mentions and concepts into a shared semantic space. On the development set, the hybrid reranker achieves Accuracy@1 = 0.3840, outperforming the bi-encoder (Accuracy@1 = 0.3298). The bi-encoder system was used for official submission and ranked third overall in the competition. Our analysis highlights the complementary strengths of both retrieval-based and embedding-based normalization strategies."
}