<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="manousogiannis-etal-2019-give">
    <titleInfo>
        <title>Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Emmanouil</namePart>
        <namePart type="family">Manousogiannis</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sepideh</namePart>
        <namePart type="family">Mesbah</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Alessandro</namePart>
        <namePart type="family">Bozzon</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Selene</namePart>
        <namePart type="family">Baez Santamaria</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Robert</namePart>
        <namePart type="given">Jan</namePart>
        <namePart type="family">Sips</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop &amp; Shared Task</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Davy</namePart>
            <namePart type="family">Weissenbacher</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Graciela</namePart>
            <namePart type="family">Gonzalez-Hernandez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper describes the system that team MYTOMORROWS-TU DELFT developed for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task 3, for the end-to-end normalization of ADR tweet mentions to their corresponding MEDDRA codes. For the first two steps, we reuse a state-of-the art approach, focusing our contribution on the final entity-linking step. For that we propose a simple Few-Shot learning approach, based on pre-trained word embeddings and data from the UMLS, combined with the provided training data. Our system (relaxed F1: 0.337-0.345) outperforms the average (relaxed F1 0.2972) of the participants in this task, demonstrating the potential feasibility of few-shot learning in the context of medical text normalization.</abstract>
    <identifier type="citekey">manousogiannis-etal-2019-give</identifier>
    <identifier type="doi">10.18653/v1/W19-3219</identifier>
    <location>
        <url>https://aclanthology.org/W19-3219</url>
    </location>
    <part>
        <date>2019-08</date>
        <extent unit="page">
            <start>114</start>
            <end>116</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts
%A Manousogiannis, Emmanouil
%A Mesbah, Sepideh
%A Bozzon, Alessandro
%A Baez Santamaria, Selene
%A Sips, Robert Jan
%Y Weissenbacher, Davy
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F manousogiannis-etal-2019-give
%X This paper describes the system that team MYTOMORROWS-TU DELFT developed for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task 3, for the end-to-end normalization of ADR tweet mentions to their corresponding MEDDRA codes. For the first two steps, we reuse a state-of-the art approach, focusing our contribution on the final entity-linking step. For that we propose a simple Few-Shot learning approach, based on pre-trained word embeddings and data from the UMLS, combined with the provided training data. Our system (relaxed F1: 0.337-0.345) outperforms the average (relaxed F1 0.2972) of the participants in this task, demonstrating the potential feasibility of few-shot learning in the context of medical text normalization.
%R 10.18653/v1/W19-3219
%U https://aclanthology.org/W19-3219
%U https://doi.org/10.18653/v1/W19-3219
%P 114-116

Markdown (Informal)

[Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts](https://aclanthology.org/W19-3219) (Manousogiannis et al., ACL 2019)

• Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts (Manousogiannis et al., ACL 2019)

ACL

• Emmanouil Manousogiannis, Sepideh Mesbah, Alessandro Bozzon, Selene Baez Santamaria, and Robert Jan Sips. 2019. Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 114–116, Florence, Italy. Association for Computational Linguistics.