SHS Web Conf.
Volume 88, 2020International Scientific Forum “Issues of Modern Linguistics and the Study of Foreign Languages in the Era of Artificial Intelligence (dedicated to World Science Day for Peace and Development)” (LLT Forum 2020)
|Number of page(s)||8|
|Section||Conference 1 - Fundamental Problems of Modern Theoretical and Applied Linguistics in an Interdisciplinary Aspect in the era of High Technologies|
|Published online||24 December 2020|
Lexical Features of Text Complexity: the case of Russian academic texts
Kazan Federal University, Kremlyovskaya St, 18, Kazan, Republic of Tatarstan, 420008, Russian Federation
* Corresponding author: firstname.lastname@example.org
The work presented in this paper is a part of an ongoing project that investigates academic text features indicative of its complexity at different grade levels. In this study we examine comparative complexity of Social science texts used in Russian secondary and high schools. Based on the metrics of ten descriptive and four lexical features assessed for seven classroom textbooks we claim lexical diversity, frequency, abstractness and the number of terminological units to be statistically significant predictors of text complexity. The total size of the Corpus of over 160.000 tokens comprising two sets of textbooks ranging from the 5th to the 11th grades provides a satisfactory level of its representativeness and as such a solid foundation for statistical validity of the results. We employ RusAC, an online text analyzer, to compute lexical features of texts and the effect of the four lexical features on text complexity is confirmed with a mixed analysis of variance. The study fills a gap both in corpus linguistics as regards a systematic approach to Russian academic texts and in text complexity studies as regards the description of secondary and high school textbooks.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.