<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ujiie-etal-2021-end">
    <titleInfo>
        <title>End-to-end Biomedical Entity Linking with Span-based Dictionary Matching</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Shogo</namePart>
        <namePart type="family">Ujiie</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hayate</namePart>
        <namePart type="family">Iso</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shuntaro</namePart>
        <namePart type="family">Yada</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shoko</namePart>
        <namePart type="family">Wakamiya</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Eiji</namePart>
        <namePart type="family">Aramaki</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 20th Workshop on Biomedical Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="given">Bretonnel</namePart>
            <namePart type="family">Cohen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sophia</namePart>
            <namePart type="family">Ananiadou</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Junichi</namePart>
            <namePart type="family">Tsujii</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Disease name recognition and normalization is a fundamental process in biomedical text mining. Recently, neural joint learning of both tasks has been proposed to utilize the mutual benefits. While this approach achieves high performance, disease concepts that do not appear in the training dataset cannot be accurately predicted. This study introduces a novel end-to-end approach that combines span representations with dictionary-matching features to address this problem. Our model handles unseen concepts by referring to a dictionary while maintaining the performance of neural network-based models. Experiments using two major datasaets demonstrate that our model achieved competitive results with strong baselines, especially for unseen concepts during training.</abstract>
    <identifier type="citekey">ujiie-etal-2021-end</identifier>
    <identifier type="doi">10.18653/v1/2021.bionlp-1.18</identifier>
    <location>
        <url>https://aclanthology.org/2021.bionlp-1.18</url>
    </location>
    <part>
        <date>2021-06</date>
        <extent unit="page">
            <start>162</start>
            <end>167</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T End-to-end Biomedical Entity Linking with Span-based Dictionary Matching
%A Ujiie, Shogo
%A Iso, Hayate
%A Yada, Shuntaro
%A Wakamiya, Shoko
%A Aramaki, Eiji
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 20th Workshop on Biomedical Language Processing
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F ujiie-etal-2021-end
%X Disease name recognition and normalization is a fundamental process in biomedical text mining. Recently, neural joint learning of both tasks has been proposed to utilize the mutual benefits. While this approach achieves high performance, disease concepts that do not appear in the training dataset cannot be accurately predicted. This study introduces a novel end-to-end approach that combines span representations with dictionary-matching features to address this problem. Our model handles unseen concepts by referring to a dictionary while maintaining the performance of neural network-based models. Experiments using two major datasaets demonstrate that our model achieved competitive results with strong baselines, especially for unseen concepts during training.
%R 10.18653/v1/2021.bionlp-1.18
%U https://aclanthology.org/2021.bionlp-1.18
%U https://doi.org/10.18653/v1/2021.bionlp-1.18
%P 162-167

Markdown (Informal)

[End-to-end Biomedical Entity Linking with Span-based Dictionary Matching](https://aclanthology.org/2021.bionlp-1.18) (Ujiie et al., BioNLP 2021)

• End-to-end Biomedical Entity Linking with Span-based Dictionary Matching (Ujiie et al., BioNLP 2021)

ACL

• Shogo Ujiie, Hayate Iso, Shuntaro Yada, Shoko Wakamiya, and Eiji Aramaki. 2021. End-to-end Biomedical Entity Linking with Span-based Dictionary Matching. In Proceedings of the 20th Workshop on Biomedical Language Processing, pages 162–167, Online. Association for Computational Linguistics.