Abstract: Currently, there are a huge number of works dedicated to semantic role labeling of English texts [1–3]. However, semantic role labeling of Russian texts was an unexplored area for many years due to the lack of train and test corpora. Semantic role labeling of Russian Texts was widely disseminated after the appearance of the FrameBank corpus [4]. In this approach, we analyzed the influence of the word embedding models on the quality of semantic role labeling of Russian texts. Micro- and macro- F1 scores on word2vec [5], fastText [6], ELMo [7] embedding models were calculated. The set of experiments have shown that fastText models averaged slightly better than word2vec models as applied to Russian FrameBank corpus. The higher micro- and macro- F1 scores were obtained on deep tokenized word representation model ELMo in relation to classical shallow embedding models.

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The article discusses the construction of a visual solution of boundary value problems for a linear differential equation of the second order by the tridiagonal sweep method in VBA in Excel. Creating macros in VBA allows you to visualize solutions to such boundary value problems.

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A new approach related to the extraction of terms of relative geological age from the metadata of scientific geological publications is described. Based on the developed and adapted approaches and technological solutions, a set of macros is implemented that implements the functions of searching, extracting and adding new metadata to scientific publications.

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This study examines the role of artificial intelligence (AI) in reshaping the ecosystem of digital scholarly communication, drawing on evidence from electronic libraries and large-scale knowledge aggregators. On the basis of an integrative review of recent international and Russian-language scholarship, AI is shown to be gradually evolving into a system-forming infrastructural mechanism across the life cycle of electronic collections, structuring processes of selection, digitisation, metadata creation, storage, and service-oriented resource discovery and access. In parallel, intelligent recommender systems are substantiated as an epistemic mediator influencing the configuration of scholarly reading, the distribution of research attention, and the visibility of peripheral forms of knowledge within the spatial–linguistic architecture of science. It is demonstrated that algorithmic personalisation cannot be reduced to improved search convenience; rather, it participates in constructing relevance norms, linguistic and regional hierarchies, and new regimes for interpreting collections. The effects identified make it possible to conceptualise algorithmic mediation at the intersection of the micro level of research identity and the macro level of the global distribution of scholarly knowledge, while also underscoring the need for reflexive governance of recommender loops in order to preserve epistemic diversity and enhance the transparency of libraries’ digital infrastructures.