  In this paper, we present Russian language datasets in the digital humanities
domain for the evaluation of word embedding techniques or similar language
modeling and feature learning algorithms. The datasets are split into two task
types, word intrusion and word analogy, and contain 31362 task units in total.
The characteristics of the tasks and datasets are that they build upon small,
domain-specific corpora, and that the datasets contain a high number of named
entities. The datasets were created manually for two fantasy novel book series
("A Song of Ice and Fire" and "Harry Potter"). We provide baseline evaluations
with popular word embedding models trained on the book corpora for the given
tasks, both for the Russian and English language versions of the datasets.
Finally, we compare and analyze the results and discuss specifics of Russian
language with regards to the problem setting.
