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.
Highlights
The area of subjects and objects of scientific activity that are united by similar directions of research or a common subject area will be called a scientific subject space
This paper has considered two specific problems that were solved on the basis of probabilistic thematic models with appropriate regularizers
A systematic analysis was performed for the probabilistic thematic model of presenting text documents, in particular scientific documents with the use of specific subject terms that were represented by n-grams
Summary
The area of subjects and objects of scientific activity that are united by similar directions of research or a common subject area will be called a scientific subject space. The subjects of such spaces are scientists, who are usually united in scientific communities. This is due to the fact that the list of terms that characterise the area is constantly adjusted due to new research and the loss of relevance of other studies. To identify the boundaries of these spaces, it is necessary to use tools that can highlight the characteristics of the spaces without pre-setting them. It is possible to use the methods of probabilistic latent semantic analysis
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