Аннотация
In this paper, we trained distributional models (patterns) for text preprocessing in Word2vec and Glove. Three variants of text preprocessing were used to train distributional patterns. Based on the implemented distribution model Word2Vec, a vector representation was obtained for a cluster-separated test sample of 30 news items. All variants of the weighted average calculation of the vector representation of texts were considered. Two-stage clustering was carried out. After training the Doc2Vec model on normalized documents, a vector representation was obtained for each document. The following news about the same event was selected for the test, but from different sources. A 2-dimensional “factual cube” was analyzed.