@inproceedings{sakib-etal-2025-nlp,
    title = "{NLP}-{DU} at {S}em{E}val-2025 Task 11: Analyzing Multi-label Emotion Detection",
    author = "Sakib, Sadman  and
      Faiak, Ahaj  and
      Arean, Abdullah Ibne Hanif  and
      Shifa, Fariha Anjum",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.semeval-1.169/",
    pages = "1269--1275",
    ISBN = "979-8-89176-273-2",
    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."
}