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
- Editors:
- Piek Vossen, Christiane Fellbaum
- Venue:
- GWC
- SIG:
- SIGLEX
- Publisher:
- Global Wordnet Association
- Note:
- Pages:
- 151–159
- Language:
- URL:
- https://aclanthology.org/2019.gwc-1.19
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/2019.gwc-1.19.pdf