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.

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The main goal of this paper was to improve topic modelling algorithms by introducing automatic topic labelling, a procedure which chooses a label for a cluster of words in a topic. Topic modelling is a widely used statistical technique which allows to reveal internal conceptual organization of text corpora. We have chosen an unsupervised graph-based method and elaborated it with regard to Russian. The proposed algorithm consists of two stages: candidate generation by means of PageRank and morphological filters, and candidate ranking. Our experiments on a corpus of encyclopedic texts on linguistics has shown the advantages of labelled topic models for NLP applications.

Abstract. The paper describes the results of experiments on the development of a statistical model of the Russian text corpus on musicology. We construct a topic model which is based on Latent Dirichlet Allocation and process corpus data with the help of GenSim statistical toolkit. Results achieved in course of experiments allow to distinguish general and special topics which describe conceptual structure of the corpus in question and to analyse paradigmatic and syntagmatic relations between lemmata within topics.

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