Abstract
The study considers the possibilities of using latent semantic analysis for the tasks of identifying scientific subject spaces and evaluating the completeness of covering the results of dissertation research by science degree seekers.A probabilistic thematic model was built to make it possible to cluster the publications of scholars in scientific areas, taking into account the citation network, which was an important step for solving the problem of identifying scientific subject spaces. As a result of constructing the model, the problem of increasing instability of clustering the citation graph in connection with a decrease in the number of clusters was solved. This problem would arise when combining clusters built on the basis of citation graph clustering, taking into account the similarity of abstracts of scientific publications.In the article, the presentation of text documents is described based on a probabilistic thematic model using n-grams. A probabilistic thematic model was built for the task of determining the completeness of covering the materials of an author’s dissertation research in scientific publications. The approximate values of the threshold coefficients were calculated to evaluate whether the articles of an author included the research provisions that were reflected in the text of the author’s abstract of the dissertation. The probabilistic thematic model for an author’s publications was practised on the basis of the BigARTM tool. Using the constructed model and with the help of a special regularizer, a matrix was found to evaluate the relevance of topics specified by the segments of an author’s dissertation abstracts to documents that are produced by the author’s publications.Important aspects of the possibilities of using latent semantic analysis were studied to identify tasks of scientific subject spaces and to reveal the completeness of covering the results of dissertation research science degree seekers.