SuperSim: a test set for word similarity and relatedness in Swedish

Simon Hengchen

Nina Tahmasebi

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Ingår i: Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa), May 31-June 2, 2021.

Linköping Electronic Conference Proceedings 178:27, s. 268-275

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Publicerad: 2021-05-21

ISBN: 978-91-7929-614-8

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


Language models are notoriously difficult to evaluate. We release SuperSim, a large-scale similarity and relatedness test set for Swedish built with expert human judgements. The test set is composed of 1,360 word-pairs independently judged for both relatedness and similarity by five annotators. We evaluate three different models (Word2Vec, fastText, and GloVe) trained on two separate Swedish datasets, namely the Swedish Gigaword corpus and a Swedish Wikipedia dump, to provide a baseline for future comparison. We will release the fully annotated test set, code, models, and data.


semantics, word embeddings, language models, evaluation, test set


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