Introducing Two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness

Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu


Abstract
We present two novel datasets for the low-resource language Vietnamese to assess models of semantic similarity: ViCon comprises pairs of synonyms and antonyms across word classes, thus offering data to distinguish between similarity and dissimilarity. ViSim-400 provides degrees of similarity across five semantic relations, as rated by human judges. The two datasets are verified through standard co-occurrence and neural network models, showing results comparable to the respective English datasets.
Anthology ID:
N18-2032
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
199–205
Language:
URL:
https://aclanthology.org/N18-2032
DOI:
10.18653/v1/N18-2032
Bibkey:
Cite (ACL):
Kim Anh Nguyen, Sabine Schulte im Walde, and Ngoc Thang Vu. 2018. Introducing Two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 199–205, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Introducing Two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness (Nguyen et al., NAACL 2018)
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PDF:
https://aclanthology.org/N18-2032.pdf