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.

Abstract: This review discusses methods and algorithms for increasing linked data expressiveness which are prepared for Web publication. The main approaches to the enrichment of ontologies are considered, the methods on which they are based and the tools for implementing the corresponding methods are described.The main stage in the general scheme of the related data life cycle in a cloud of Linked Open Data is the stage of building a set of related RDF- triples. To improve the classification of data and the analysis of their quality, various methods are used to increase the expressiveness of related data. The main ideas of these methods are concerned with the enrichment of existing ontologies (an expansion of the basic scheme of knowledge) by adding or improving terminological axioms. Enrichment methods are based on methods used in various fields, such as knowledge representation, machine learning, statistics, natural language processing, analysis of formal concepts, and game theory.