@inproceedings{markchom-etal-2023-uor,
    title = "{U}o{R}-{NCL} at {S}em{E}val-2023 Task 1: Learning Word-Sense and Image Embeddings for Word Sense Disambiguation",
    author = "Markchom, Thanet  and
      Liang, Huizhi  and
      Gitau, Joyce  and
      Liu, Zehao  and
      Ojha, Varun  and
      Taylor, Lee  and
      Bonnici, Jake  and
      Alshadadi, Abdullah",
    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-1.3",
    doi = "10.18653/v1/2023.semeval-1.3",
    pages = "16--22",
    abstract = "In SemEval-2023 Task 1, a task of applying Word Sense Disambiguation in an image retrieval system was introduced. To resolve this task, this work proposes three approaches: (1) an unsupervised approach considering similarities between word senses and image captions, (2) a supervised approach using a Siamese neural network, and (3) a self-supervised approach using a Bayesian personalized ranking framework. According to the results, both supervised and self-supervised approaches outperformed the unsupervised approach. They can effectively identify correct images of ambiguous words in the dataset provided in this task.",
}

In SemEval-2023 Task 1, a task of applying Word Sense Disambiguation in an image retrieval system was introduced. To resolve this task, this work proposes three approaches: (1) an unsupervised approach considering similarities between word senses and image captions, (2) a supervised approach using a Siamese neural network, and (3) a self-supervised approach using a Bayesian personalized ranking framework. According to the results, both supervised and self-supervised approaches outperformed the unsupervised approach. They can effectively identify correct images of ambiguous words in the dataset provided in this task.
Markchom, Thanet Liang, Huizhi Gitau, Joyce Liu, Zehao Ojha, Varun Taylor, Lee Bonnici, Jake Alshadadi, Abdullah
UoR-NCL at SemEval-2023 Task 1: Learning Word-Sense and Image Embeddings for Word Sense Disambiguation