研究生: |
張裕嘉 Yu-Chia Chang |
---|---|
論文名稱: |
以自然語言處理技術為基礎之自動搭配詞寫作輔助器 An Automatic Collocation Writing Assistant for Taiwanese EFL Learners Based on NLP Technology |
指導教授: |
張俊盛
Dr. Jason S. Chang |
口試委員: | |
學位類別: |
碩士 Master |
系所名稱: |
電機資訊學院 - 資訊系統與應用研究所 Institute of Information Systems and Applications |
論文出版年: | 2005 |
畢業學年度: | 93 |
語文別: | 英文 |
論文頁數: | 48 |
中文關鍵詞: | 搭配詞錯誤分析 、第一語言影響 、自動錯誤改正 |
外文關鍵詞: | Miscollocation Analysis, L1 Influence, Automatic Error Correction |
相關次數: | 點閱:3 下載:0 |
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搭配詞(Collocation)在語言學習上已經被認定為相當重要的學習面向,但是這個領域在外語教學上卻常常被忽略。過去的研究指出以英語為外語的學習者(EFL Learners)在搭配詞的表現上往往不盡理想;但搭配詞卻是流利英文寫作的重要特徵,同時學習良好的寫作者往往能夠在寫作時,較為正確且頻繁地使用搭配詞。
在不同的搭配詞類型中,動名結構的搭配詞對於台灣的學習者而言是最難掌握的。許多對於學習者搭配詞錯誤的研究也指出,學習者的第一語言對於其搭配詞的使用上往往有很深的影響。因此,我們針對台灣的英語學習者,開發了一個線上搭配詞協助工具,期望能夠幫助學生偵錯與改正因第一語言干擾而造成的搭配詞錯誤。
系統會透過學習者第一語言與第二語言去尋找共通翻譯,並藉此提供相關的正確搭配詞作為搭配詞改正的建議。我們的方法首先會利用自然語言處理(NLP)的技術,將使用者文章中的句子作詞塊分析(Chunking) 以擷取出其需要受測的動名結構搭配詞,並利用雙語字典的中英文翻譯,針對受測的英文動詞部分去產生含有共通翻譯的英文動詞候選字(Candidtaes)。爾後,將這些候選字與原來的名詞相結合,進而利用搭配詞詞表將正確的搭配詞組合選出,做為系統回饋的建議。此系統預期能夠自動的標示出學生的搭配詞錯誤,並針對學生想要表達的原意提供改正的建議。我們期望此線上搭配詞協助工具能夠幫助學生學習搭配詞的使用,並將習得知識轉而應用到他們未來的英語寫作上。
Collocation has been acknowledged as a crucial aspect in vocabulary learning but the area has long been neglected in foreign language teaching. Previous work in the literature reveals that EFL learners were seriously deficient in collocations which are a hallmark of near native fluency in learner’s writing; good learner-writers used collocation more appropriately and frequently than poor learner-writers did. Among different types of collocation, the verb-noun (V-N) type was found to be particularly difficult for Taiwanese learners to master. Studies on EFL learners’ miscollocations also indicated that learners’ first language would heavily influence their production of collocations. We develop an online collocation aid for EFL writers in Taiwan, aiming at detection and correction of learners’ miscollocations attributable to L1 interference. Relevant correct collocation as feedback messages is suggested according to the mutual translations between learner’s L1 and L2. The system utilizes natural language processing (NLP) techniques to chunk sentences in order to extract V-N collocations in input texts, and to derive a list of candidate English verbs that share the same Chinese translations via processing of bilingual corpora. After combining nouns with those candidate verbs as V-N pairs, the system makes use of a reference English corpus to exclude the inappropriate V-N pairs so as to single out the proper collocations. The system can pinpoint the miscollocation and prompt the learner with suggestions of correct collocations that the learner intends to write but misuses. It is hoped that this online assistant can facilitate EFL learner-writers’ collocation use and transfer that knowledge to their future writing.
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