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.
@article{arxiv.1804.05388,
title = {Introducing two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness},
author = {Kim Anh Nguyen and Sabine Schulte im Walde and Ngoc Thang Vu},
journal= {arXiv preprint arXiv:1804.05388},
year = {2018}
}
Comments
The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2018)