<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="upadhyaya-etal-2026-software">
    <titleInfo>
        <title>The Software Mention Detection and Coreference Resolution Shared Task 2026</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sharmila</namePart>
        <namePart type="family">Upadhyaya</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Wolfgang</namePart>
        <namePart type="family">Otto</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Julia</namePart>
        <namePart type="family">Matela</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Frank</namePart>
        <namePart type="family">Krüger</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Stefan</namePart>
        <namePart type="family">Dietze</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Georg</namePart>
            <namePart type="family">Rehm</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Stefan</namePart>
            <namePart type="family">Dietze</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Danilo</namePart>
            <namePart type="family">Dessi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Diana</namePart>
            <namePart type="family">Maynard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sonja</namePart>
            <namePart type="family">Schimmler</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA Language Resources Association (ELRA)</publisher>
            <place>
                <placeTerm type="text">Palma, Mallorca (Spain)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Software is referenced in research papers in many different ways: full names, abbreviations, misspellings, versioned names, or indirect references via websites and citations. This makes it hard to link mentions to a single software entity, which in turn limits large-scale analyses and knowledge graph construction. The Software Mention Detection and Coreference Resolution (SOMD) shared task 2026, organized at the Natural Scientific Language Processing (NSLP) workshop at LREC 2026, focuses on clustering software mentions that refer to the same software entity. We provide three subtasks covering gold mentions, automatically extracted mentions, and mentions sampled at scale from large-scale publications. Systems are evaluated with established coreference metrics (MUC, B³, CEAFe) and their CoNLL average. This paper describes the task setup, datasets, evaluation, baseline, and the observed patterns in participant submissions, and outlines future directions for scalable software mention coreference resolution. The shared task was concluded with total five registered participants, with total 43 submissions for all subtasks. Finally, two system papers were submitted with competitive performance against baselines.</abstract>
    <identifier type="citekey">upadhyaya-etal-2026-software</identifier>
    <identifier type="doi">10.63317/3koortigjkfr</identifier>
    <location>
        <url>https://aclanthology.org/2026.nslp-1.24/</url>
    </location>
    <part>
        <date>2026-05</date>
        <extent unit="page">
            <start>247</start>
            <end>254</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T The Software Mention Detection and Coreference Resolution Shared Task 2026
%A Upadhyaya, Sharmila
%A Otto, Wolfgang
%A Matela, Julia
%A Krüger, Frank
%A Dietze, Stefan
%Y Rehm, Georg
%Y Dietze, Stefan
%Y Dessi, Danilo
%Y Maynard, Diana
%Y Schimmler, Sonja
%S Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F upadhyaya-etal-2026-software
%X Software is referenced in research papers in many different ways: full names, abbreviations, misspellings, versioned names, or indirect references via websites and citations. This makes it hard to link mentions to a single software entity, which in turn limits large-scale analyses and knowledge graph construction. The Software Mention Detection and Coreference Resolution (SOMD) shared task 2026, organized at the Natural Scientific Language Processing (NSLP) workshop at LREC 2026, focuses on clustering software mentions that refer to the same software entity. We provide three subtasks covering gold mentions, automatically extracted mentions, and mentions sampled at scale from large-scale publications. Systems are evaluated with established coreference metrics (MUC, B³, CEAFe) and their CoNLL average. This paper describes the task setup, datasets, evaluation, baseline, and the observed patterns in participant submissions, and outlines future directions for scalable software mention coreference resolution. The shared task was concluded with total five registered participants, with total 43 submissions for all subtasks. Finally, two system papers were submitted with competitive performance against baselines.
%R 10.63317/3koortigjkfr
%U https://aclanthology.org/2026.nslp-1.24/
%U https://doi.org/10.63317/3koortigjkfr
%P 247-254

Markdown (Informal)

[The Software Mention Detection and Coreference Resolution Shared Task 2026](https://aclanthology.org/2026.nslp-1.24/) (Upadhyaya et al., NSLP 2026)

• The Software Mention Detection and Coreference Resolution Shared Task 2026 (Upadhyaya et al., NSLP 2026)

ACL

• Sharmila Upadhyaya, Wolfgang Otto, Julia Matela, Frank Krüger, and Stefan Dietze. 2026. The Software Mention Detection and Coreference Resolution Shared Task 2026. In Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026, pages 247–254, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).