@inproceedings{heavey-etal-2023-stfx,
    title = "{S}t{FX}-{NLP} at {S}em{E}val-2023 Task 4: Unsupervised and Supervised Approaches to Detecting Human Values in Arguments",
    author = "Heavey, Ethan  and
      King, Milton  and
      Hughes, James",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.29",
    doi = "10.18653/v1/2023.semeval-1.29",
    pages = "205--211",
    abstract = "In this paper, we discuss our models applied to Task 4: Human Value Detection of SemEval 2023, which incorporated two different embedding techniques to interpret the data. Preliminary experiments were conducted to observe important word types. Subsequently, we explored an XGBoost model, an unsupervised learning model, and two Ensemble learning models were then explored. The best performing model, an ensemble model employing a soft voting technique, secured the 34th spot out of 39 teams, on a class imbalanced dataset. We explored the inclusion of different parts of the provided knowledge resource and found that considering only specific parts assisted our models.",
}

In this paper, we discuss our models applied to Task 4: Human Value Detection of SemEval 2023, which incorporated two different embedding techniques to interpret the data. Preliminary experiments were conducted to observe important word types. Subsequently, we explored an XGBoost model, an unsupervised learning model, and two Ensemble learning models were then explored. The best performing model, an ensemble model employing a soft voting technique, secured the 34th spot out of 39 teams, on a class imbalanced dataset. We explored the inclusion of different parts of the provided knowledge resource and found that considering only specific parts assisted our models.
Heavey, Ethan King, Milton Hughes, James
StFX-NLP at SemEval-2023 Task 4: Unsupervised and Supervised Approaches to Detecting Human Values in Arguments