Comparative Study of Word Associations in Social Networks Corpora by means of Distributional Semantics Models for Russian

A. A. Antipenko, O. A. Mitrofanova


The paper discusses results of the experiment on automatic extraction of associative relations from corpora of Russian texts from Facebook and Pikabu social networks by means of distributional semantic models. The choice of linguistic data for analysis, namely, social networks texts, is determined by the specificity of polylogic internet-discourse which combines traits of written and colloquial speech. We put forward the hypothesis on the possibility of reproduction of associative test technique in the experiments with distributional semantic models. Experiments were carried out with the help of algorithms and tools of Distributional Semantics. We extracted associations for lexemes expressing key concepts of Russian-specific world view. The procedure was performed by means of Word2Vec (CBOW and Skip-gram) neural network architectures. We carried out linguistic analysis of the output data and compared it with the associations described in the Russian Associative Dictionary, Russian regional association database (Siberia and Fare East) and the Russian Distributional Thesaurus. Results achieved in course of experiments allow to make conclusions on the dynamic of Russian-specific language consciousness of contemporary social network users. We worked out and implemented the procedure of quantitative evaluation of data extracted from different sources. We found evidence on the specialization of lexicographic resources and distributional semantic models as regards paradigmatic and syntagmatic relations. Experimental data allowed to carry out linguistic analysis of contemporary Russian-specific world view of social networks users and to reveal tendencies in its development.

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