@inproceedings{pan-etal-2022-zuo,
    title = "Zuo Zhuan {A}ncient {C}hinese Dataset for Word Sense Disambiguation",
    author = "Pan, Xiaomeng  and
      Wang, Hongfei  and
      Oka, Teruaki  and
      Komachi, Mamoru",
    editor = "Ippolito, Daphne  and
      Li, Liunian Harold  and
      Pacheco, Maria Leonor  and
      Chen, Danqi  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop",
    month = jul,
    year = "2022",
    address = "Hybrid: Seattle, Washington + Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.naacl-srw.17/",
    doi = "10.18653/v1/2022.naacl-srw.17",
    pages = "129--135",
    abstract = "Word Sense Disambiguation (WSD) is a core task in Natural Language Processing (NLP). Ancient Chinese has rarely been used in WSD tasks, however, as no public dataset for ancient Chinese WSD tasks exists. Creation of an ancient Chinese dataset is considered a significant challenge because determining the most appropriate sense in a context is difficult and time-consuming owing to the different usages in ancient and modern Chinese. Actually, no public dataset for ancient Chinese WSD tasks exists. To solve the problem of ancient Chinese WSD, we annotate part of Pre-Qin (221 BC) text \textit{Zuo Zhuan} using a copyright-free dictionary to create a public sense-tagged dataset. Then, we apply a simple Nearest Neighbors (k-NN) method using a pre-trained language model to the dataset. Our code and dataset will be available on GitHub."
}