<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sakib-etal-2025-nlp">
    <titleInfo>
        <title>NLP-DU at SemEval-2025 Task 11: Analyzing Multi-label Emotion Detection</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sadman</namePart>
        <namePart type="family">Sakib</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ahaj</namePart>
        <namePart type="family">Faiak</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Abdullah</namePart>
        <namePart type="given">Ibne</namePart>
        <namePart type="given">Hanif</namePart>
        <namePart type="family">Arean</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Fariha</namePart>
        <namePart type="given">Anjum</namePart>
        <namePart type="family">Shifa</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Sara</namePart>
            <namePart type="family">Rosenthal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Aiala</namePart>
            <namePart type="family">Rosá</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Debanjan</namePart>
            <namePart type="family">Ghosh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marcos</namePart>
            <namePart type="family">Zampieri</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Vienna, Austria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-273-2</identifier>
    </relatedItem>
    <abstract>This paper describes NLP-DU’s entry to SemEval-2025 Task 11 on multi-label emotion detection. We investigated the efficacy of transformer-based models and propose an ensemble approach that combines multiple models. Our experiments demonstrate that the ensemble outperforms individual models under the dataset constraints, yielding superior performance on key evaluation metrics. These findings underscore the potential of ensemble techniques in enhancing multi-label emotion detection and contribute to the broader understanding of emotion analysis in natural language processing.</abstract>
    <identifier type="citekey">sakib-etal-2025-nlp</identifier>
    <location>
        <url>https://aclanthology.org/2025.semeval-1.169/</url>
    </location>
    <part>
        <date>2025-07</date>
        <extent unit="page">
            <start>1269</start>
            <end>1275</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T NLP-DU at SemEval-2025 Task 11: Analyzing Multi-label Emotion Detection
%A Sakib, Sadman
%A Faiak, Ahaj
%A Arean, Abdullah Ibne Hanif
%A Shifa, Fariha Anjum
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F sakib-etal-2025-nlp
%X This paper describes NLP-DU’s entry to SemEval-2025 Task 11 on multi-label emotion detection. We investigated the efficacy of transformer-based models and propose an ensemble approach that combines multiple models. Our experiments demonstrate that the ensemble outperforms individual models under the dataset constraints, yielding superior performance on key evaluation metrics. These findings underscore the potential of ensemble techniques in enhancing multi-label emotion detection and contribute to the broader understanding of emotion analysis in natural language processing.
%U https://aclanthology.org/2025.semeval-1.169/
%P 1269-1275

Markdown (Informal)

[NLP-DU at SemEval-2025 Task 11: Analyzing Multi-label Emotion Detection](https://aclanthology.org/2025.semeval-1.169/) (Sakib et al., SemEval 2025)

• NLP-DU at SemEval-2025 Task 11: Analyzing Multi-label Emotion Detection (Sakib et al., SemEval 2025)

ACL

• Sadman Sakib, Ahaj Faiak, Abdullah Ibne Hanif Arean, and Fariha Anjum Shifa. 2025. NLP-DU at SemEval-2025 Task 11: Analyzing Multi-label Emotion Detection. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 1269–1275, Vienna, Austria. Association for Computational Linguistics.