(18.204.2.190) 您好!臺灣時間:2021/04/22 07:46
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:徐千惠
研究生(外文):Chien-hui Hsu
論文名稱:主要漢字形聲字發音規則探勘與視覺化
論文名稱(外文):Primary Chinese Semantic-Phonetic Compounds Pronunciation Rules Mining and Visualization
指導教授:蔡孟峰蔡孟峰引用關係
指導教授(外文):Meng-feng Tsai
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:61
中文關鍵詞:漢語識字教學形聲字聲符多層次關聯式規則探勘關聯式規則視覺化
外文關鍵詞:Chinese TeachingSemantic-Phonetic CompoundsPhonetic ComponentMultiple-level Association Rule MiningAssociation Rule Mining Visualization
相關次數:
  • 被引用被引用:1
  • 點閱點閱:414
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:66
  • 收藏至我的研究室書目清單書目收藏:1
全球有五分之一的人口使用漢語做為母語,加上中華地區經濟崛起的緣故,愈來愈多人將漢語做為第二外語來學習,擴大了漢語的學習市場。進一步來看,現今海外華人人數約為五千萬人;而在台灣的大陸與外籍配偶從2002年的二十二萬人次,已增加到四十四萬人次,其中外籍配偶約占十四萬六千人次。由這些現象可看出華語教學的需求量與重要性日漸增加。
語言學習可分成口語能力和文字能力的訓練,若是以漢語做為第二語言的學習者已有基本的口語能力,但是由於漢字字形不直接表音的特性,使得學習者難以培養漢字的文字能力。反觀屬於拼音文字系統的英語,若是以英語做為第二語言的學習者同樣具備基本的口語能力,欲進一步加強其文字能力,學習者只須學會英語的拼音系統後,就能邊念單字邊把它拼寫(spell)出來。
為符合漢語學習者的學習背景,採用「部件教學法」幫助學習者構築現代漢字,進而學習到部件延伸的漢字,是為基礎有效率的教學方法。由此找出漢字表音和表意的線索,形聲字是最能符合此種特徵的漢字結構。形聲字在現代漢語通用字中佔八成,大多是由一個表意的形符加上一個表音的聲符。若能強調由聲符表音的線索,將能輔助漢字識字教學與漢字研究。
本研究強調聲符表音的線索,以關聯式規則探勘出形聲字發音規則。本研究進一步地找出影響形聲字發音的關鍵因素,以此探勘主要的形聲字發音規則。再輔以漢語音韻學的知識,建立漢字發音的階層架構,進行多層次形聲字發音規則探勘,藉此幫助漢語學習者與教學研究歸納形聲字發音的情形,帶領他們從宏觀或是細微的角度了解漢字發音的脈絡。最後用視覺化的方式呈現這些規則,並設計簡單、好記、一目了然的系統輔助漢字識字教學與漢字研究。

There are twenty percent of people who learn Chinese as a mother tongue in the world. Because China’s economy is growing rapidly in the recent years, learning Chinese as a second language is regarded as a more and more important training, and the number of Chinese learners increases multiple times, too. Moreover, the overseas Chinese population is about fifty million. Because the society of Taiwan has transferred, the foreign spouse and mainland China spouse population has risen from 230 thousand in 2002 to 440 thousand now in Taiwan. The foreign spouse population is more than 146 thousand. We can see that the demand and the importance of Chinese teaching have increased continuously from such a tendency.
Language learning can be divided into the oral language learning and the written language learning. For the learners who learn Chinese as a second language, even if they have the basic Chinese oral abilities, they are still hard to read and write Chinese characters since the shapes of Chinese characters do not represent their pronunciations directly. On the other hand, if English learners have the basic English oral abilities, they are easy to read and write English through learning English alphabetic system.
In order to fit the background of the Chinese learners, the phonetic component teaching is adopted to assist Chinese learners in composing modern Chinese characters and learning the derived characters further. It is the basic and efficient Chinese teaching method. In the phonetic component teaching, the learners can find the clues to both the pronunciations and the meanings of Chinese characters, and semantic-phonetic compounds are exactly proper to teach the Chinese learners. There are 80.5% semantic-phonetic compounds in the 7000 common Chinese characters, and most of them are formed with one semantic component and one phonetic component. If we can emphasize the clues to the pronunciations of Chinese characters, the phonetic component teaching and Chinese researches will be improved.
Association rule mining was applied to discover such knowledge, and the results are called the pronunciation rules of semantic-phonetic compounds. This approach found the key factors of phonetic components which have the strong connection with the pronunciations of semantic-phonetic compounds and then provided Chinese learners and Chinese researchers with the primary pronunciation rules of semantic-phonetic compounds. With the knowledge of Chinese linguistics, we constructed the hierarchical Chinese pronunciation structure and discovered the hierarchical pronunciation rules. These rules are the overview of the pronunciations of semantic-phonetic compounds and aid both Chinese learning and Chinese researches. Therefore, they can learn the pronunciations of Chinese characters not only in the general aspect but the specific aspect. These rules were represented in visualization and the simple, memorable and understandable system was designed to assist both the Chinese literacy teaching and Chinese researches.
中文摘要 i
英文摘要 ii
誌謝 iv
目錄 v
圖目錄 vi
表目錄 vii
一、 緒論 1
論文架構 4
二、 相關研究 5
2-1 資料探勘 5
2-2 關聯式規則探勘 6
2-3 關聯式規則視覺化 7
2-3-1 散播平面圖(scatter plot) 7
2-3-2 以圖解為基礎的視覺化(graph-based visualization) 7
2-3-3 平行座標圖(parallel coordinates plots) 8
2-3-4 雙層圖(double decker plot) 9
2-3-5 以矩陣為基礎的視覺化(matrix-based visualization) 10
2-4 漢字發音表示法 12
2-5 漢語教學研究 14
三、 形聲字重要發音規則探勘與視覺化 18
3-1 資料前處理 18
3-2 漢字發音階層架構 20
3-3 主要的形聲字多層次發音規則探勘 22
3-4 主要的形聲字發音規則視覺化 24
四、 主要貢獻成果 26
五、 結論與未來方向 36
參考文獻 37
〔1〕 張良民,「全球華語學習熱潮與僑教發展」,研習資訊,23:2,9-15頁,2006年。
〔2〕 林季苗,「漢語教學四大原則與法國經驗」,華語文教學研究,8:2,65-79頁,2011年8月。
〔3〕段玉裁《說文解字注》,十一版,黎明文化事業股份有限公司,台北,民國八十三年七月。
〔4〕 中研院文獻處理實驗室,「漢字構形資料庫」,http://cdp.sinica.edu.tw/cdphanzi/。
〔5〕 Jiawei H. and Micheline K., Data Mining: Concepts and Techniques, 2nd ed., Morgan Kaufmann Publishers, March 2006.
〔6〕 Unwin, A., Hofmann, H., and Bernt, K., “The TwoKey Plot for Multiple Association Rules Control,” in PKDD'01: Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 472-483, Springer-Verlag London, UK, 2001.
〔7〕 Bastian, M., Heymann, S., and Jacomy, M., “Gephi: An Open Source Software for Exploring and Manipulating Networks,” in International AAAI Conference on Weblogs and Social Media, pp. 361-362, 2009.
〔8〕 Yang, L., “Visualizing Frequent Itemsets, Association Rules, and Sequential Patterns in Parallel Coordinates,” in ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications, pp. 21-30, Montreal, Canada, May 18-21, 2003.
〔9〕 Heike Hofmann, Arno P. J. M. Siebes, and Adalbert F. X. Wilhelm, “Visualizing Association Rules with Interactive Mosaic Plots,” in KDD '00 Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 227-235, New York, NY, USA, 2000.
〔10〕Michael Hahsler and Sudheer Chelluboina, “Visualizing association rules in hierarchical groups,” In Computing Science and Statistics, Vol. 42, 42nd Symposium on the Interface: Statistical, Machine Learning and Visualization Algorithms (Interface 2011), the Interface Foundation of North America, June 2011.
〔11〕 Gupta, G., Strehl, A., and Ghosh, J., “Distance Based Clustering of Association Rules,” in Intelligent Engineering Systems through Artificial Neural Networks (Proceedings of ANNIE 1999), pp. 759-764, 1999.
〔12〕Lee, C.-Y., Tsai, J.-L., Su, E. C.-I., Tzeng, O. J.-L., & Hung, D. L., “Consistency, regularity and frequency effects in naming Chinese characters”, Language and Linguistics, 6(1), pp. 75-107, 2005.
〔13〕郝明義,中文妙方(ChineseCUBES),中文妙方公司,台北,2013年取自http://www.chinesecubes.com/
〔14〕張嘉惠, 李淑瑩, 林書彥, 黃嘉毅, 陳志銘,《以最佳化及機率分佈判斷漢字聲符之研究》,ROCLING, 2010。
〔15〕張嘉惠, 林書彥,《聲符部件排序與形聲字發音規則探勘》,ROCLING, 2011。
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔