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

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

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arXiv · 1804.05388

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

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

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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.

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Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu. 2018-04-15. Introducing two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness. https://arxiv.org/abs/1804.05388

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