%0 Conference Proceedings %T Augmenting Chinese WordNet semantic relations with contextualized embeddings %A Tseng, Yu-Hsiang %A Hsieh, Shu-Kai %Y Vossen, Piek %Y Fellbaum, Christiane %S Proceedings of the 10th Global Wordnet Conference %D 2019 %8 July %I Global Wordnet Association %C Wroclaw, Poland %F tseng-hsieh-2019-augmenting %X 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. %U https://aclanthology.org/2019.gwc-1.19 %P 151-159