Augmenting Chinese WordNet semantic relations with contextualized embeddings

Yu-Hsiang Tseng, Shu-Kai Hsieh


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.
Anthology ID:
2019.gwc-1.19
Volume:
Proceedings of the 10th Global Wordnet Conference
Month:
July
Year:
2019
Address:
Wroclaw, Poland
Venue:
GWC
SIG:
Publisher:
Global Wordnet Association
Note:
Pages:
151–159
Language:
URL:
https://aclanthology.org/2019.gwc-1.19
DOI:
Bibkey:
Cite (ACL):
Yu-Hsiang Tseng and Shu-Kai Hsieh. 2019. Augmenting Chinese WordNet semantic relations with contextualized embeddings. In Proceedings of the 10th Global Wordnet Conference, pages 151–159, Wroclaw, Poland. Global Wordnet Association.
Cite (Informal):
Augmenting Chinese WordNet semantic relations with contextualized embeddings (Tseng & Hsieh, GWC 2019)
Copy Citation:
PDF:
https://aclanthology.org/2019.gwc-1.19.pdf