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  • 标题:Analysing corpus-based criterial conjunctions for automatic proficiency classification
  • 本地全文:下载
  • 作者:Ángeles Zarco-Tejada ; Carmen Noya Gallardo ; Mª Carmen Merino Ferradá
  • 期刊名称:Journal of English Studies
  • 印刷版ISSN:1576-6357
  • 电子版ISSN:1695-4300
  • 出版年度:2016
  • 卷号:14
  • 页码:215-237
  • DOI:10.18172/jes.3090
  • 出版社:Universidad de La Rioja
  • 摘要:The linguistic profiling of L2 learning texts can be taken as a model for automatic proficiency assessment of new texts. But proficiency levels are distinguished by many different linguistic features among which the use of cohesive devices can be a criterial element for level distinctions, either in the number of conjunctions used (quantitative) and/or in the type and variety of them (qualitative). We have carried such an analysis with a subgroup of the CLEC (CEFR-levelled English Corpus) using Coh-Metrix, a tool for computing computational cohesion and coherence metrics for written and spoken texts, but our results suggest that automatic proficiency level assessment needs a deeper examination of conjunctions that should rely on the analysis of conjunction-types use and conjunction varieties, with an analysis of lexical choice. A variable based on familiarity ranks could help to predict cohesive levels proficiencyoriented.
  • 关键词:Cohesion; language assessment; corpus linguistics; L2 English learning texts; linguistic profiling; Coh-Metrix.
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