二语写作中句法复杂性自动分析与人工分析的比较

IF 1.6 2区 文学 N/A LANGUAGE & LINGUISTICS International Journal of Corpus Linguistics Pub Date : 2022-10-17 DOI:10.1075/ijcl.20181.cha
Quang Hồng Châu, Bram Bulté
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引用次数: 2

摘要

句法复杂性测量的自动化工具越来越多地用于分析各种第二语言语料库,尽管这些工具最初是为高级学习者编写的文本开发和测试的。本研究通过对荷兰语学习者所写的80篇文本的语料库进行人工分析和自动分析,探讨了自动测量初级和中低二语英语数据复杂性的可靠性。我们的定量和定性分析表明,自动化复杂性测量的可靠性在很大程度上受到学习者错误、解析器错误和Tregex模式欠生成的影响。我们还演示了在计算工具和人类注释器之间对齐分析单元定义的重要性。为了提高自动化分析的可靠性,建议对系统进行某些修改,并且在自动分析之前对非高级L2英语数据进行预处理。
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A comparison of automated and manual analyses of syntactic complexity in L2 English writing
Automated tools for syntactic complexity measurement are increasingly used for analyzing various kinds of second language corpora, even though these tools were originally developed and tested for texts produced by advanced learners. This study investigates the reliability of automated complexity measurement for beginner and lower-intermediate L2 English data by comparing manual and automated analyses of a corpus of 80 texts written by Dutch-speaking learners. Our quantitative and qualitative analyses reveal that the reliability of automated complexity measurement is substantially affected by learner errors, parser errors, and Tregex pattern undergeneration. We also demonstrate the importance of aligning the definitions of analytical units between the computational tool and human annotators. In order to enhance the reliability of automated analyses, it is recommended that certain modifications are made to the system, and non-advanced L2 English data are preprocessed prior to automated analyses.
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来源期刊
CiteScore
3.30
自引率
0.00%
发文量
43
期刊介绍: The International Journal of Corpus Linguistics (IJCL) publishes original research covering methodological, applied and theoretical work in any area of corpus linguistics. Through its focus on empirical language research, IJCL provides a forum for the presentation of new findings and innovative approaches in any area of linguistics (e.g. lexicology, grammar, discourse analysis, stylistics, sociolinguistics, morphology, contrastive linguistics), applied linguistics (e.g. language teaching, forensic linguistics), and translation studies. Based on its interest in corpus methodology, IJCL also invites contributions on the interface between corpus and computational linguistics.
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