<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="unnikrishnan-etal-2022-halelab">
    <titleInfo>
        <title>HaleLab_NITK@SMM4H’22: Adaptive Learning Model for Effective Detection, Extraction and Normalization of Adverse Drug Events from Social Media Data</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Reshma</namePart>
        <namePart type="family">Unnikrishnan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sowmya</namePart>
        <namePart type="family">Kamath S</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ananthanarayana</namePart>
        <namePart type="family">V. S.</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop &amp; Shared Task</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Graciela</namePart>
            <namePart type="family">Gonzalez-Hernandez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Davy</namePart>
            <namePart type="family">Weissenbacher</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Gyeongju, Republic of Korea</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">unnikrishnan-etal-2022-halelab</identifier>
    <location>
        <url>https://aclanthology.org/2022.smm4h-1.27</url>
    </location>
    <part>
        <date>2022-10</date>
        <extent unit="page">
            <start>95</start>
            <end>97</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T HaleLab_NITK@SMM4H’22: Adaptive Learning Model for Effective Detection, Extraction and Normalization of Adverse Drug Events from Social Media Data
%A Unnikrishnan, Reshma
%A Kamath S, Sowmya
%A V. S., Ananthanarayana
%Y Gonzalez-Hernandez, Graciela
%Y Weissenbacher, Davy
%S Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F unnikrishnan-etal-2022-halelab
%X 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.
%U https://aclanthology.org/2022.smm4h-1.27
%P 95-97

Markdown (Informal)

[HaleLab_NITK@SMM4H’22: Adaptive Learning Model for Effective Detection, Extraction and Normalization of Adverse Drug Events from Social Media Data](https://aclanthology.org/2022.smm4h-1.27) (Unnikrishnan et al., SMM4H 2022)

• HaleLab_NITK@SMM4H’22: Adaptive Learning Model for Effective Detection, Extraction and Normalization of Adverse Drug Events from Social Media Data (Unnikrishnan et al., SMM4H 2022)

ACL

• Reshma Unnikrishnan, Sowmya Kamath S, and Ananthanarayana V. S.. 2022. HaleLab_NITK@SMM4H’22: Adaptive Learning Model for Effective Detection, Extraction and Normalization of Adverse Drug Events from Social Media Data. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 95–97, Gyeongju, Republic of Korea. Association for Computational Linguistics.