有感於世界對於華語文學習的需求與日俱增，但在華語學習環境中，卻沒有像英文托福考試使用的e-Rater這類的工具，可以幫助華語文教師或學生進行教學或學習，因此研製一個給華語文領域使用的作文分級系統，希望能對此有所助益。
本論文之研究使用Stanford parser作為文法剖析器，開發出數個文法相關特徵，並以貝氏機率為機器學習之模型，實作出華語作文分級系統。
本研究所開發出的系統可達到93%的正確性，對於華語作文以分數作為分級的方法，可達到不錯的效果，亦可在華語老師的教學上或華語測驗裡實際使用。

Due to the learning boom of CFL, the needs of learning equipment increased. However, there is no such tool like e-Rater for TOEFL in the CFL learning field for Chinese teaching instructors and students to use. In this study, we tent to build up an automated essay scoring system for CFL learners leading to a better CFL learning environment.
The system we developed in the study used Stanford Parser as a grammar parser to analyze and parse sentences to design some grammar features that could fit the system. We used Bayesian theorem as a machine learning model. By integrating features to the model, we built up a Chinese essay scoring system for CFL.
The system could reach to 93% on the adjacent accuracy in rating the scores of essays and could literally use for the practical needs in CFL teaching or test.

第一章緒論 1
第一節研究動機 1
第二節 研究目的 5
第三節 研究限制 6
第二章文獻探討 7
第一節外語寫作學習 7
第二節AES自動評分系統 11
第三節文法剖析技術 13
第四節CRIE與Coh-Metrix 17
第三章系統設計 19
第一節資料前處理 21
第二節文法剖析 22
第三節文章特徵 25
第四節 評分系統 29
第四章研究方法 35
第一節 研究工具 35
第二節 研究設計 45
第三節 研究目的與結果 50
第四節 研究討論 74
第五章結論與未來發展 77
第一節 結論 77
第二節 未來發展 79
參考文獻 81
附錄一 Coh-Metrix2.0 文本分析工具特徵整理表 89
 

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