@inproceedings{heavey-etal-2024-stfx,
    title = "{S}t{FX}-{NLP} at {S}em{E}val-2024 Task 9: {BRAINTEASER}: Three Unsupervised Riddle-Solvers",
    author = "Heavey, Ethan  and
      Hughes, James  and
      King, Milton",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Tayyar Madabushi, Harish  and
      Da San Martino, Giovanni  and
      Rosenthal, Sara  and
      Ros{\'a}, Aiala},
    booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.semeval-1.5/",
    doi = "10.18653/v1/2024.semeval-1.5",
    pages = "28--33",
    abstract = "In this paper, we explore three unsupervised learning models that we applied to Task 9: BRAINTEASER of SemEval 2024. Two of these models incorporate word sense disambiguation and part-of-speech tagging, specifically leveraging SensEmBERT and the Stanford log-linear part-of-speech tagger. Our third model relies on a more traditional language modelling approach. The best performing model, a bag-of-words model leveraging word sense disambiguation and part-of-speech tagging, secured the 10th spot out of 11 places on both the sentence puzzle and word puzzle subtasks."
}