From Corpus to Classroom: Teaching Semi-technical Business English Vocabulary

Lidan Chen
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Abstract

Semi-technical vocabulary has been considered a challenging and neglected area of English for Specific Purposes (ESP) instruction. This paper employs AntConc to extract a keyword list of a self-compiled business English textbook corpus (BETC). Through manual identification from corpus keywords, we focus on semi-technical vocabulary, addressing the fundamental question of “how to identify”. We also draw upon pedagogical materials from the academic sub-corpora within the Corpus of Contemporary American English (COCA) to design a corpus-based language pedagogy (CBLP) lesson. This lesson serves as a model for instructing the multifaceted meanings and diverse patterns of the semi-technical word “default” across various disciplines and contexts, addressing the question of “how to teach”. Our research leverages the rich resources provided by pedagogical corpora, offering in-depth analysis, including collocation, colligation, semantic preference, and semantic prosody, as effective teaching aids. In doing so, it promotes interdisciplinary, comparative, and exploratory teaching and learning of semi-technical business English vocabulary. By bridging the gap between corpus analysis and classroom instruction, it provides innovative strategies for educators in the field of business English, and by implication, in various ESP disciplines.
从语料库到课堂:半技术性商务英语词汇教学
半技术词汇一直被认为是专门用途英语(ESP)教学中一个具有挑战性且被忽视的领域。本文采用 AntConc 提取自编商务英语教材语料库(BETC)的关键词列表。通过人工识别语料库中的关键词,我们将重点放在半技术词汇上,解决了 "如何识别 "这一根本问题。我们还借鉴了当代美国英语语料库(COCA)中学术子语料库的教学材料,设计了基于语料库的语言教学法(CBLP)课程。该课程可作为在不同学科和语境中指导半专业术语 "默认 "的多层面含义和多样化模式的范例,解决 "如何教 "的问题。我们的研究利用教学语料库提供的丰富资源,提供深入分析,包括搭配、拼合、语义偏好和语义拟声,作为有效的教学辅助工具。在此过程中,它促进了半技术性商务英语词汇的跨学科、比较性和探索性教学。通过弥合语料库分析与课堂教学之间的差距,该书为商务英语领域的教育工作者提供了创新策略,同时也暗示了 ESP 各学科的教育工作者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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