<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="dai-karimi-2022-detecting">
    <titleInfo>
        <title>Detecting Entities in the Astrophysics Literature: A Comparison of Word-based and Span-based Entity Recognition Methods</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Xiang</namePart>
        <namePart type="family">Dai</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sarvnaz</namePart>
        <namePart type="family">Karimi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the first Workshop on Information Extraction from Scientific Publications</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Tirthankar</namePart>
            <namePart type="family">Ghosal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sergi</namePart>
            <namePart type="family">Blanco-Cuaresma</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alberto</namePart>
            <namePart type="family">Accomazzi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Robert</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Patton</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Felix</namePart>
            <namePart type="family">Grezes</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Thomas</namePart>
            <namePart type="family">Allen</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>Information Extraction from scientific literature can be challenging due to the highly specialised nature of such text. We describe our entity recognition methods developed as part of the DEAL (Detecting Entities in the Astrophysics Literature) shared task. The aim of the task is to build a system that can identify Named Entities in a dataset composed by scholarly articles from astrophysics literature. We planned our participation such that it enables us to conduct an empirical comparison between word-based tagging and span-based classification methods. When evaluated on two hidden test sets provided by the organizer, our best-performing submission achieved F1 scores of 0.8307 (validation phase) and 0.7990 (testing phase).</abstract>
    <identifier type="citekey">dai-karimi-2022-detecting</identifier>
    <location>
        <url>https://aclanthology.org/2022.wiesp-1.9</url>
    </location>
    <part>
        <date>2022-11</date>
        <extent unit="page">
            <start>78</start>
            <end>83</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Detecting Entities in the Astrophysics Literature: A Comparison of Word-based and Span-based Entity Recognition Methods
%A Dai, Xiang
%A Karimi, Sarvnaz
%Y Ghosal, Tirthankar
%Y Blanco-Cuaresma, Sergi
%Y Accomazzi, Alberto
%Y Patton, Robert M.
%Y Grezes, Felix
%Y Allen, Thomas
%S Proceedings of the first Workshop on Information Extraction from Scientific Publications
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online
%F dai-karimi-2022-detecting
%X Information Extraction from scientific literature can be challenging due to the highly specialised nature of such text. We describe our entity recognition methods developed as part of the DEAL (Detecting Entities in the Astrophysics Literature) shared task. The aim of the task is to build a system that can identify Named Entities in a dataset composed by scholarly articles from astrophysics literature. We planned our participation such that it enables us to conduct an empirical comparison between word-based tagging and span-based classification methods. When evaluated on two hidden test sets provided by the organizer, our best-performing submission achieved F1 scores of 0.8307 (validation phase) and 0.7990 (testing phase).
%U https://aclanthology.org/2022.wiesp-1.9
%P 78-83

Markdown (Informal)

[Detecting Entities in the Astrophysics Literature: A Comparison of Word-based and Span-based Entity Recognition Methods](https://aclanthology.org/2022.wiesp-1.9) (Dai & Karimi, WIESP 2022)

• Detecting Entities in the Astrophysics Literature: A Comparison of Word-based and Span-based Entity Recognition Methods (Dai & Karimi, WIESP 2022)

ACL

• Xiang Dai and Sarvnaz Karimi. 2022. Detecting Entities in the Astrophysics Literature: A Comparison of Word-based and Span-based Entity Recognition Methods. In Proceedings of the first Workshop on Information Extraction from Scientific Publications, pages 78–83, Online. Association for Computational Linguistics.