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In this paper, we present Russian language datasets in the digital humanitiesdomain for the evaluation of word embedding techniques or similar languagemodeling and feature learning algorithms. The datasets are split into two tasktypes, 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 namedentities. The datasets were created manually for two fantasy novel book series("A Song of Ice and Fire" and "Harry Potter"). We provide baseline evaluationswith popular word embedding models trained on the book corpora for the giventasks, both for the Russian and English language versions of the datasets.Finally, we compare and analyze the results and discuss specifics of Russianlanguage with regards to the problem setting.

Russian Language Datasets in the Digitial Humanities Domain and Their Evaluation with Word Embeddings