<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="babasaheb-madasamy-2025-scaler">
    <titleInfo>
        <title>SCaLER@ALTA 2025: Hybrid and Bi-Encoder Approaches for Adverse Drug Event Mention Normalization</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Shelke</namePart>
        <namePart type="given">Akshay</namePart>
        <namePart type="family">Babasaheb</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anand</namePart>
        <namePart type="given">Kumar</namePart>
        <namePart type="family">Madasamy</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 23rd Annual Workshop of the Australasian Language Technology Association</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Jonathan</namePart>
            <namePart type="given">K</namePart>
            <namePart type="family">Kummerfeld</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Aditya</namePart>
            <namePart type="family">Joshi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mark</namePart>
            <namePart type="family">Dras</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Sydney, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">1834-7037</identifier>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">babasaheb-madasamy-2025-scaler</identifier>
    <location>
        <url>https://aclanthology.org/2025.alta-main.17/</url>
    </location>
    <part>
        <date>2025-11</date>
        <extent unit="page">
            <start>224</start>
            <end>229</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T SCaLER@ALTA 2025: Hybrid and Bi-Encoder Approaches for Adverse Drug Event Mention Normalization
%A Babasaheb, Shelke Akshay
%A Madasamy, Anand Kumar
%Y Kummerfeld, Jonathan K.
%Y Joshi, Aditya
%Y Dras, Mark
%S Proceedings of the 23rd Annual Workshop of the Australasian Language Technology Association
%D 2025
%8 November
%I Association for Computational Linguistics
%C Sydney, Australia
%@ 1834-7037
%F babasaheb-madasamy-2025-scaler
%X 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.
%U https://aclanthology.org/2025.alta-main.17/
%P 224-229

Markdown (Informal)

[SCaLER@ALTA 2025: Hybrid and Bi-Encoder Approaches for Adverse Drug Event Mention Normalization](https://aclanthology.org/2025.alta-main.17/) (Babasaheb & Madasamy, ALTA 2025)

• SCaLER@ALTA 2025: Hybrid and Bi-Encoder Approaches for Adverse Drug Event Mention Normalization (Babasaheb & Madasamy, ALTA 2025)

ACL

• Shelke Akshay Babasaheb and Anand Kumar Madasamy. 2025. SCaLER@ALTA 2025: Hybrid and Bi-Encoder Approaches for Adverse Drug Event Mention Normalization. In Proceedings of the 23rd Annual Workshop of the Australasian Language Technology Association, pages 224–229, Sydney, Australia. Association for Computational Linguistics.