Word-formation complexity: a learner corpus-based study

IF 1.5 0 LANGUAGE & LINGUISTICS Russian Journal of Linguistics Pub Date : 2022-06-29 DOI:10.22363/2687-0088-31187
O. Lyashevskaya, Julia Vyacheslavovna Pyzhak, Olga Vinogradova
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Abstract

This article explores the word-formation dimension of learner text complexity which indicates how skilful the non-native speakers are in using more and less complex - and varied - derivational constructions. In order to analyse the association between complexity and writing accuracy in word formation as well as interactive effects of task type, text register, and native language background, we examine the materials of the REALEC corpus of English essays written by university students with Russian L1. We present an approach to measure derivational complexity based on the classification of suffixes offered in Bauer and Nation (1993) and then compare the complexity results and the number of word formation errors annotated in the texts. Starting with the hypothesis that with increasing complexity the number of errors will decrease, we apply statistical analysis to examine the association between complexity and accuracy. We found, first, that the use of more advanced word-formation suffixes affects the number of errors in texts. Second, different levels of suffixes in the hierarchy affect derivation accuracy in different ways. In particular, the use of irregular derivational models is positively associated with the number of errors. Third, the type of examination task and expected format and register of writing should be taken into consideration. The hypothesis holds true for regular but infrequent advanced suffixal models used in more formal descriptive essays associated with an academic register. However, for less formal texts with lower academic register requirements, the hypothesis needs to be amended.
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构词复杂性:基于学习者语料库的研究
本文探讨了学习者语篇复杂性的构词维度,它反映了非本族语使用者在使用或多或少复杂或多样的衍生结构方面的熟练程度。为了分析构词法的复杂性和写作准确性之间的关系,以及任务类型、文本域和母语背景的交互作用,我们研究了REALEC语料库中俄语母语大学生写的英语论文的材料。我们提出了一种基于Bauer和Nation(1993)提供的后缀分类来衡量衍生复杂性的方法,然后比较复杂性结果和文本中注释的构词法错误的数量。假设随着复杂性的增加,错误的数量会减少,我们应用统计分析来检验复杂性和准确性之间的关系。我们发现,首先,使用更高级的构词法后缀会影响文本中错误的数量。其次,不同层次的词缀对派生精度的影响也不同。特别是,不规则衍生模型的使用与误差的数量呈正相关。第三,应考虑考试任务的类型和预期的写作格式和写作范围。该假设适用于与学术注册相关的更正式的描述性文章中使用的常规但不常见的高级后缀模型。然而,对于学术注册要求较低的非正式文本,该假设需要修正。
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来源期刊
Russian Journal of Linguistics
Russian Journal of Linguistics Arts and Humanities-Language and Linguistics
CiteScore
3.00
自引率
33.30%
发文量
43
审稿时长
14 weeks
期刊最新文献
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