- 學位論文
以中文為母語之在台研究生學術英語寫作語料中動詞名詞錯誤搭配之研究
A Study on Verb-Noun Miscollocations in an Academic Writing Corpus of Chinese-Speaking Postgraduate Students in Taiwan
摘要
研究生要在攻讀碩士的階段取得一定程度的成功並成為學術社群的一員，必定需要同時培養撰寫研究論文和期刊文章的技能。但是對於以英語作為外語學習(EFL)的學生來說，發展這些技能往往會構成相當艱辛的挑戰。除了掌握英語學術寫作的慣例和相關的語言要求對許多學生來說令人不知所措，對於正在學習英語作為第二語言或外語的學生而言，詞組搭配使用方式也經常帶來其獨特的難題，因為學生可能不清楚哪些詞組搭配已經成語化，或哪些詞組具有相互替代性。另外一個常出現的問題是，即使研究生擁有上述的意識，他們仍然會對於哪些詞語會產生可接受的詞組搭配有一定程度的不確定性。連在英語檢定上成功取得高級認證的學生，仍然有可能發現在構思與使用可接受的詞組會有搭配上的困難，在動詞-名詞（V-N）詞組搭配上尤其棘手。 本研究利用語彙資料庫分析調查了來自商業、教育、工程、語言、科學和社會科學等12個不同學門的中的研究生/EFL學習者所犯的詞組搭配錯誤，並透過分析這些研究生所撰寫的英文碩士論文之內容組成12個子語料庫，再合併成一個更大的學習者語料庫做為研究用途。上述的學習者語料庫被與參考語料庫COCA-Academic進行了對照。首先，所有出現在學習者語料庫中的名詞，總共有759個名詞的列表被生成。列表中，在學習者語料庫中出現1000次的詞彙，則進一步使用Sketch Engine的Word Sketch Difference功能進行錯誤的詞彙搭配。這次檢查共識別出了190個最常出問題的名詞和327種不同的錯誤詞組搭配模式，共計2909個詞元。其中絕大多數的錯誤詞組搭配是動詞-名詞的錯誤搭配來自於研究生的中文母語的影響而產生的。一些錯誤詞組搭配在特定的子語料庫中出現，通常也顯示出這個學門所導致的特定性錯誤。本研究所收集與彙整的資訊，希望能對未來於該主題的研究有所幫助，同時也為相關的教學方法提供新的見解。
並列摘要
To succeed in graduate school and become a member of a disciplinary community, postgraduate students need to develop skills in writing research papers and journal articles. However, for students who are studying English as a foreign language (EFL), developing these skills can prove to be an immense challenge. Mastering the conventions of academic writing and the associated linguistic demands may prove overwhelming for many students. Collocations can present a unique challenge to students who are learning English as a second or foreign language. Students may be unaware of which collocations are more idiomatic and which allow substitution. If students do possess this awareness, they may still be unsure which words will produce acceptable collocations. Even when learners reach advanced levels of proficiency in a second language, they may still find producing acceptable collocations troublesome. Verb-Noun (V-N) collocations are particularly problematic. This study utilized corpus analysis to investigate miscollocations made by EFL learners who were postgraduate students from 12 diverse academic disciplines in the broader fields of business, education, engineering, language, science, and social science. Master’s theses written in English from these postgraduate students were compiled to form 12 subcorpora that were then combined into one larger learner corpus. This learner corpus was compared to a reference corpus, COCA-Academic. First, a list of all the nouns in the learner corpus was generated. A total of 759 nouns, each of which appeared in the learner corpus at a frequency of 1,000 times, were examined with Sketch Engine’s Word Sketch Difference function. This examination identified 190 problematic nouns and 327 different miscollocation patterns, with a total of 2,909 tokens. The great majority of these miscollocations were V-N miscollocations and most were produced due to the influence of the students L1, Chinese. There were some miscollocations which occurred in a number of the subcorpora and some erroneous collocations also showed disciplinary specificity. Information collected from the study will hopefully contribute to the body of research on this topic and provide insights to pedagogical approaches.