The article is focused on automatic development and ranking of a large corpus for Russian paraphrase generation which proves to be the first corpus of such type in Russian computational linguistics. Existing manually annotated paraphrase datasets for Russian are limited to small-sized ParaPhraser corpus and ParaPlag which are suitable for a set of NLP tasks, such as paraphrase and plagiarism detection, sentence similarity and relatedness estimation, etc. Due to size restrictions, these datasets can hardly be applied in end-to-end text generation solutions. Meanwhile, paraphrase generation requires a large amount of training data. In our study we propose a solution to the problem: we collect, rank and evaluate a new publicly available headline paraphrase corpus (ParaPhraser Plus), and then perform text generation experiments with manual evaluation on automatically ranked corpora using the Universal Transformer architecture.

The article is focused on automatic development and ranking of a large corpus for Russian paraphrase generation which proves to be the first corpus of such type in Russian computational linguistics. Existing manually annotated paraphrase datasets for Russian are limited to small-sized ParaPhraser corpus and ParaPlag which are suitable for a set of NLP tasks, such as paraphrase and plagiarism detection, sentence similarity and relatedness estimation, etc. Due to size restrictions, these datasets can hardly be applied in end-to-end text generation solutions. Meanwhile, paraphrase generation requires a large amount of training data. In our study we propose a solution to the problem: we collect, rank and evaluate a new publicly available headline paraphrase corpus (ParaPhraser Plus), and then perform text generation experiments with manual evaluation on automatically ranked corpora using the Universal Transformer architecture.

Automatic natural language processing that goes beyond the bag-of-words model requires an understanding of semantic and syntactic relationships between language components. This work attempts to address the following issues: 1) describe a set of Russian prepositional structures as an interconnected system; 2) collect corpus-based statistics reflecting preposition meanings and their hierarchy; and 3) describe meanings of prepositions acting as grammatical connectors for content words. Preposition meanings are described using categories based on G. A. Zolotova’s concept of syntaxeme. The results obtained are based on word frequencies extracted from corpora of contemporary Russian texts. This work describes the key objectives, approaches, methods, novelty, and findings of the research. The key findings include grammar and ontology of the main Russian prepositions, research into secondary prepositions (prepositions derived from content words), preposition database with a network interface, and a set of automated procedures aiding in ‘working out’ the meaning of a preposition.

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