Russian dictionary with concreteness/abstractness indices

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The demand for a Russian dictionary with indices of abstractness/concreteness of words has been expressed in a number of areas including linguistics, psychology, neurophysiology and cognitive studies focused on imaging concepts in human cognitive systems. Although dictionaries of abstractness/concreteness were compiled for a number of languages, Russian has been recently viewed as an under-resourced language for the lack of one. The Laboratory of Quantitative Linguistics of Kazan Federal University has implemented two methods of compiling dictionaries of abstract/concrete words, i.e. respondents survey and extrapolation of human estimates with the help of an original computer program. In this article, we provide a detailed description of the methodology used for assessing abstractness/concreteness of words by native Russian respondents, as well as control algorithms validating the survey quality. The implementation of the methodology has enabled us to create a Russian dictionary (1500 words) with indices of concreteness/abstractness of words, including those missing in the Russian Semantic Dictionary by N.Yu. Shvedova (1998). We have also created three versions of a machine dictionary of abstractness/concreteness based on the extrapolation of the respondents' ratings. The third, most accurate version contains 22,000 words and has been compiled with the use of a modern deep learning technology of neural networks. The paper provides statistical characteristics (histograms of the distribution of ratings, dispersion, etc.) of both the machine dictionary and the dictionary obtained by interviewing informants. The quality of the machine dictionary was validated on a test set of words by means of contrasting machine and human evaluations with the latter viewed as more credible. The purpose of the paper is to give a detailed description of the methodology employed to create a concrete/abstract dictionary, as well as to demonstrate the methodology of its application in theoretical and applied research on concrete examples. The paper shows the practical use of this vocabulary in six case studies: predicting the complexity of school textbooks as a function of the share of abstract words; comparing abstractness indices of Russian-English equivalents; assessing concreteness/abstractness of polysemantic words; contrasting ratings of different age groups of respondents; contrasting ratings of respondents with different levels of education; analyzing concepts of "concreteness” and “specificity”.

作者简介

Valery Solovyev

Kazan (Volga region) Federal University

Email: maki.solovyev@mail.ru
ORCID iD: 0000-0003-4692-2564

Doctor Habil. of Physics and Mathematics, Professor, Chief Researcher of the Text Analytics Research Laboratory

18 Kremlevskaya St., Kazan, 420008, Russia

Yulia Volskaya

Kazan (Volga region) Federal University

Email: kovaleva95julia@mail.ru
ORCID iD: 0000-0001-8276-5864

Assistant Professor of the Department of Applied and Experimental Linguistics, and Junior Research Fellow of the Neurocognitive Research Laboratory

18 Kremlevskaya St., Kazan, 420008, Russia

Mariia Andreeva

Kazan (Volga region) Federal University; Kazan State Medical University

Email: lafruta@mail.ru
ORCID iD: 0000-0002-5760-0934

holds a PhD degree in Philology and is Associate Professor of the Department of Foreign Languages

18 Kremlevskaya St., Kazan, 420008, Russia; 49 Butlerov St., Kazan, 420012, Russia

Artem Zaikin

Kazan (Volga region) Federal University

编辑信件的主要联系方式.
Email: kaskrin@gmail.com
ORCID iD: 0000-0002-5596-3176

Doctor of Physics and Mathematics and Associate Professor of the Department of Mathematical Statistics

18 Kremlevskaya St., Kazan, 420008, Russia

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