  Semantic relatedness between words is one of the core concepts in natural
language processing, thus making semantic evaluation an important task. In this
paper, we present a semantic model evaluation dataset: SimRelUz - a collection
of similarity and relatedness scores of word pairs for the low-resource Uzbek
language. The dataset consists of more than a thousand pairs of words carefully
selected based on their morphological features, occurrence frequency, semantic
relation, as well as annotated by eleven native Uzbek speakers from different
age groups and gender. We also paid attention to the problem of dealing with
rare words and out-of-vocabulary words to thoroughly evaluate the robustness of
semantic models.
