<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yano-etal-2023-distant">
    <titleInfo>
        <title>DISTANT: Distantly Supervised Entity Span Detection and Classification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ken</namePart>
        <namePart type="family">Yano</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Makoto</namePart>
        <namePart type="family">Miwa</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sophia</namePart>
        <namePart type="family">Ananiadou</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks</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">Sophia</namePart>
            <namePart type="family">Ananiadou</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="family">Cohen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Toronto, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We propose a distantly supervised pipeline NER which executes entity span detection and entity classification in sequence named DISTANT (DIstantly Supervised enTity spAN deTection and classification).The former entity span detector extracts possible entity mention spans by the distant supervision. Then the later entity classifier assigns each entity span to one of the positive entity types or none by employing a positive and unlabeled (PU) learning framework. Two models were built based on the pre-trained SciBERT model and fine-tuned with the silver corpus generated by the distant supervision. Experimental results on BC5CDR and NCBI-Disease datasets show that our method outperforms the end-to-end NER baselines without PU learning by a large margin. In particular, it increases the recall score effectively.</abstract>
    <identifier type="citekey">yano-etal-2023-distant</identifier>
    <identifier type="doi">10.18653/v1/2023.bionlp-1.14</identifier>
    <location>
        <url>https://aclanthology.org/2023.bionlp-1.14</url>
    </location>
    <part>
        <date>2023-07</date>
        <extent unit="page">
            <start>171</start>
            <end>177</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T DISTANT: Distantly Supervised Entity Span Detection and Classification
%A Yano, Ken
%A Miwa, Makoto
%A Ananiadou, Sophia
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F yano-etal-2023-distant
%X We propose a distantly supervised pipeline NER which executes entity span detection and entity classification in sequence named DISTANT (DIstantly Supervised enTity spAN deTection and classification).The former entity span detector extracts possible entity mention spans by the distant supervision. Then the later entity classifier assigns each entity span to one of the positive entity types or none by employing a positive and unlabeled (PU) learning framework. Two models were built based on the pre-trained SciBERT model and fine-tuned with the silver corpus generated by the distant supervision. Experimental results on BC5CDR and NCBI-Disease datasets show that our method outperforms the end-to-end NER baselines without PU learning by a large margin. In particular, it increases the recall score effectively.
%R 10.18653/v1/2023.bionlp-1.14
%U https://aclanthology.org/2023.bionlp-1.14
%U https://doi.org/10.18653/v1/2023.bionlp-1.14
%P 171-177

Markdown (Informal)

[DISTANT: Distantly Supervised Entity Span Detection and Classification](https://aclanthology.org/2023.bionlp-1.14) (Yano et al., BioNLP 2023)

• DISTANT: Distantly Supervised Entity Span Detection and Classification (Yano et al., BioNLP 2023)

ACL

• Ken Yano, Makoto Miwa, and Sophia Ananiadou. 2023. DISTANT: Distantly Supervised Entity Span Detection and Classification. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 171–177, Toronto, Canada. Association for Computational Linguistics.