Аннотация:This paper presents an approach to creating interpretable word embeddings, in which each component of the vector corresponds to some interpretable semantic category. To obtain such categories, a lexico-semantic resource is used in the form of the RuWordNet semantic network, as well as a representative corpus of Russian-language texts to train vector representations. The resulting interpretable embeddings were evaluated on semantic similarity tasks.