研究生: |
林鶯 Lin, Ying |
---|---|
論文名稱: |
基於語料庫之研究-連結TED演講中的詞串與文部 A Corpus-based Study: Connecting Lexical Bundles with Moves in TED Talks |
指導教授: |
陳浩然
Chen, Hao-Jan |
學位類別: |
碩士 Master |
系所名稱: |
英語學系 Department of English |
論文出版年: | 2020 |
畢業學年度: | 108 |
語文別: | 英文 |
論文頁數: | 134 |
中文關鍵詞: | 詞串 、文步分析 、口語語料庫 、TED演講 、英語口語報告訓練 |
英文關鍵詞: | Lexical Bundle, Move Analysis, Spoken Corpus, TED Talks, English Oral Presentation Training |
DOI URL: | http://doi.org/10.6345/NTNU202000770 |
論文種類: | 學術論文 |
相關次數: | 點閱:211 下載:27 |
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本研究旨在探討TED 演講中常被使用的詞串(Lexical bundles),以了解TED 演講之語言本質及建構方式,本研究探討以下四個研究問題:(一)TED 演講中常用之詞串為何?(二)TED 演講中的詞串與學術口語詞串組合相似度為何?(三)TED演講中詞串之結構及功能為何?(四)在TED 演講不同文部(Moves)中,講者使用哪些詞串?
本研究採用並過濾Liu和Chen (2019)蒐集之TED演講語料庫,經本研究篩選後,總語料庫大小約為兩百五十萬字。為分析TED演講文步中的詞串,本研究採用Chang和Huang (2015)所建立之TED演講的文步,即TED演講之組織架構。TED演講中詞串之結構及功能使用WordSmith 7.0和Sketch Engine做為本研究使用之語料庫工具,進行資料分析。詞串與文步之連結由兩位專家依據各個詞串每次出現時之溝通目的及情境,將TED演講中之詞串與文步進行分類。除詞串與文步之連結,本研究亦分析詞串之結構及功能,並將TED 演講中之詞串與另外兩個學術口語詞串所提供之詞串表作比較。
研究結果顯示,本研究整理之TED演講語料庫中,共有119個常用詞串,全部詞串發生總次數為12,572次,主要研究結果摘述如下:
(一) 學習TED演講中的常用詞串組合也可能同時幫助理解學術課堂之內容;
(二) 百分之六十之TED演講常用詞串以動詞為基礎所組成;而TED演講之詞串平均地分布於三種主要功能類別,而其中指示詞串佔了多一點比例 (36.1%);
(三) TED演講中之常用詞串大多為多功能;
(四) TED演講中,主題發展為擁有最多詞串數之文步(66%),主題介紹則為次之(19.7%),演講者介紹位居第三位(6.5%);
(五) TED演講者在七個文步使用之詞串組合因其溝通目的而有不同之性質。
本研究針對各個文步中所使用的詞串進行詳盡之描述,並提供真實使用情境以做教學之參考,協助未來研究者更容易了解TED演講中常用之語言特質。依據研究結果本研究提供相關之啟示,本研究期許能對口語語料庫之研究有所貢獻,更提供口語教學上實用的引導及教學素材。
The current study aimed to explore the lexical bundles in TED Talks to better understand the linguistic nature of TED Talks and the way they were constructed. Four research questions were addressed in this study: 1) What are the common lexical bundles in TED Talks? 2) How are the lexical bundles in TED Talks similar to those in academic contexts? 3) What are the structures and functions of the lexical bundles in TED talks? 4) What are the lexical bundles in different moves in TED talks?
The TED Talk corpus compiled by Liu and Chen (2019) was filtered, and the resulting corpus was about 2.5 million words. To investigate the lexical bundles in different moves in TED Talks, the move framework established by Chang and Huang (2015) was adopted. Two corpus tools, WordSmith 7.0 and Sketch Engine, were employed to extract and analyze lexical bundles in TED Talks. The identified lexical bundles were assigned to the moves by two raters. In addition, structural and functional analysis of the lexical bundles were also carried out. TED Talk bundles were also compared with those found in academic contexts.
The results showed there were a total of 119 recurrent lexical bundles in TED Talks, with 12,572 bundle tokens. The major research findings were summarized as follows: 1) The lexical bundles found in TED Talks were likely to help comprehension of academic lectures. 2) About 60% of the bundles were verb-based. Also, it was found the three functional types of lexical bundles accounted for almost the same proportion, with referential bundles having slightly higher proportion (36.1%). 3) The lexical bundles in TED Talks were multi-functional. 4) The move Topic Development was assigned the most bundle tokens (66% of all), followed by Topic Introduction (19.7%) and Speaker Presentation (6.5%). 5) The seven moves were characterized by different sets of lexical bundles to achieve the specific communicative purposes. Aside from these results, the lexical bundles of each move were also illustrated in detail in actual contexts.
This research expected to offer practical pedagogical materials for instructors or learners who want to teach or master the language of TED Talks. At the same time, this study also added more knowledge to the spoken genres and described the linguistic characteristics of TED Talks.
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