Method for Generating Interpretable Embeddings Based on Superconcepts

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.