@inproceedings{tseng-hsieh-2019-augmenting,
    title = "Augmenting {C}hinese {W}ord{N}et semantic relations with contextualized embeddings",
    author = "Tseng, Yu-Hsiang  and
      Hsieh, Shu-Kai",
    editor = "Vossen, Piek  and
      Fellbaum, Christiane",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2019.gwc-1.19/",
    pages = "151--159",
    abstract = "Constructing semantic relations in WordNet has been a labour-intensive task, especially in a dynamic and fast-changing language environment. Combined with recent advancements of contextualized embeddings, this paper proposes the concept of morphology-guided sense vectors, which can be used to semi-automatically augment semantic relations in Chinese Wordnet (CWN). This paper (1) built sense vectors with pre-trained contextualized embedding models; (2) demonstrated the sense vectors computed were consistent with the sense distinctions made in CWN; and (3) predicted the potential semantically-related sense pairs with high accuracy by sense vectors model."
}