SCIENTIFIC TEXTS CLASSIFICATION BASED ON COMPRESSION OF PUBLICATION ABSTRACTS
The possibility of establishing the semantic proximity of scientific texts by the method of their automatic classification based on the compression of abstracts is investigated. The idea of the method is that compression algorithms such as PPM (prediction by partial matching) compress terminologically close texts much better than distant ones. If a core of publications (an analog of the training set) is formed for each classified topic, the best proportion of compression will indicate that the classified text belongs to the corresponding topic. 30 thematic categories were determined, each of them in the Scopus database received annotations of about 500 publications, of which 100 annotations for the core and 20 annotations for testing were selected in different ways. It was found that building a core based on highly cited publications reveals up to 12% of errors against 32% with random sampling. The quality of classification is also affected by the initial number of categories: the fewer categories participate in the classification and the more terminological differences between them, the higher its quality.