<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hiai-etal-2021-relation">
    <titleInfo>
        <title>Relation Extraction Using Multiple Pre-Training Models in Biomedical Domain</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Satoshi</namePart>
        <namePart type="family">Hiai</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kazutaka</namePart>
        <namePart type="family">Shimada</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Taiki</namePart>
        <namePart type="family">Watanabe</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Akiva</namePart>
        <namePart type="family">Miura</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tomoya</namePart>
        <namePart type="family">Iwakura</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd.</publisher>
            <place>
                <placeTerm type="text">Held Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The number of biomedical documents is increasing rapidly. Accordingly, a demand for extracting knowledge from large-scale biomedical texts is also increasing. BERT-based models are known for their high performance in various tasks. However, it is often computationally expensive. A high-end GPU environment is not available in many situations. To attain both high accuracy and fast extraction speed, we propose combinations of simpler pre-trained models. Our method outperforms the latest state-of-the-art model and BERT-based models on the GAD corpus. In addition, our method shows approximately three times faster extraction speed than the BERT-based models on the ChemProt corpus and reduces the memory size to one sixth of the BERT ones.</abstract>
    <identifier type="citekey">hiai-etal-2021-relation</identifier>
    <location>
        <url>https://aclanthology.org/2021.ranlp-1.60</url>
    </location>
    <part>
        <date>2021-09</date>
        <extent unit="page">
            <start>530</start>
            <end>537</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Relation Extraction Using Multiple Pre-Training Models in Biomedical Domain
%A Hiai, Satoshi
%A Shimada, Kazutaka
%A Watanabe, Taiki
%A Miura, Akiva
%A Iwakura, Tomoya
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F hiai-etal-2021-relation
%X The number of biomedical documents is increasing rapidly. Accordingly, a demand for extracting knowledge from large-scale biomedical texts is also increasing. BERT-based models are known for their high performance in various tasks. However, it is often computationally expensive. A high-end GPU environment is not available in many situations. To attain both high accuracy and fast extraction speed, we propose combinations of simpler pre-trained models. Our method outperforms the latest state-of-the-art model and BERT-based models on the GAD corpus. In addition, our method shows approximately three times faster extraction speed than the BERT-based models on the ChemProt corpus and reduces the memory size to one sixth of the BERT ones.
%U https://aclanthology.org/2021.ranlp-1.60
%P 530-537

Markdown (Informal)

[Relation Extraction Using Multiple Pre-Training Models in Biomedical Domain](https://aclanthology.org/2021.ranlp-1.60) (Hiai et al., RANLP 2021)

• Relation Extraction Using Multiple Pre-Training Models in Biomedical Domain (Hiai et al., RANLP 2021)

ACL

• Satoshi Hiai, Kazutaka Shimada, Taiki Watanabe, Akiva Miura, and Tomoya Iwakura. 2021. Relation Extraction Using Multiple Pre-Training Models in Biomedical Domain. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 530–537, Held Online. INCOMA Ltd..