Lexical predictors of text complexity: The case of Russian as a foreign language

Author:

Gafiyatova E.1,Galyavieva L.2,Solnyshkina M.1

Affiliation:

1. Kazan Federal University

2. Kazan State Academy of Veterinary Medicine

Abstract

   The article presents results of a comparative analysis of lexical complexity of educational texts in teaching Russian as a foreign language. The corpus size of the study is about 0.5 million words evenly distributed among six levels of language proficiency (A1-C2, Russian National System of Certification Levels of General Proficiency in Russian as a Foreign Language, further – RNSCL). The analysis algorithm is demonstrated based on B2 level texts, for which we estimated the values of the eight complexity predictors using the automatic analyzers RuLex (rulex.kpfu.ru) and RuLingva (rulingva.kpfu.ru): the number of tokens and types, sentence length, word length, lexical diversity (LD), terminological density, readability (MSIS) and frequency. B2 texts demonstrate significant differences in all the parameters, except for the word length. The validated B2 average word length is 2.26 syllables. The increase of lexical diversity from A1 to C2 is insignificant being within the range of 0.3 - 0.5. The complexity growth in RFL texts is accompanied by an increase of terminological density and the readability index. Since the RFL text is an important source of linguocultural information, the research findings may be useful to researchers, developers of educational resources and test materials, and teachers for text selection processes.

Publisher

Kazan Federal University

Subject

General Medicine

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3