Introducing two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness
Domaine:
natural language processingType de record:
paperdataset
Créateur:
NguWalVu,
Hôte:
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. The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2018)
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