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研究生:蔡秉恆
研究生(外文):Ping-Heng Tsai
論文名稱:利用文件句型關聯建構概念構圖之研究
論文名稱(外文):Using the Sentences Pattern Relationship to Build a Concept Map
指導教授:洪朝富洪朝富引用關係
指導教授(外文):Chau-Fu Hong
學位類別:碩士
校院名稱:真理大學
系所名稱:管理科學研究所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:53
中文關鍵詞:概念構圖自然語言處理文字探勘
外文關鍵詞:Concept MapNatural Language ProcessText Mining
相關次數:
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概念圖的理論在1970 時被Novak 所提及,Novak 將這種技巧應用在科學教學上,做為一種增進理解的教學技術。Novak 的設計是基於的同化理論(assimilation theory)。Ausubel 以建構式學習(constructivism learning)的觀點,強調先前知識(prior knowledge)是學習新知識的基礎框架(framework),並有不可取代的重要性。本研究主要是利用其建構概念圖的方法,將學生的文章透過電腦分析的方式轉成概念圖,透過圖形有順序的方式呈現出學生對主題的探討與想法。
在過去的研究中有許多有關於概念圖的研究以及做法,首先提出來的是利用手繪製的方式,將概念呈現於紙上,在上課之前老師要先傳授其繪製的技巧,並於上課後對學生繪製出來的概念做分析或評分,而Rebich 這位學者在2005 年時,則是提及利用這樣的方法會讓學生對概念因不懂而產生誤解(Misconception),後來隨著電腦化的興起有學者則是將其用在非結構性的文字檔案中,例如: 新聞 [Montes, Gelbukh and López,2001]、化學領域 [Liu, Navathe, Civera, Dasigi, Ram, Ciliax and Dingledine, 2005]、專利資料 [Yoon and Park, 2004]以及醫學臨床紀錄 [Zhou, Han, Chankai, Prestrud, Brooks,2006],並且透過詞與詞之間的關聯找出文章中的概念並針對這些概念的連進行探討。
另外也有部分學者將這技術應用於網站的開發上,透過專家提供一些概念字與概念的連結,協助學生在概念圖上的建置[Qiu,2003; Lee, 2004].在分析的過程中,第一次的實驗本研究導入了自然語言處理協助我們找出文件在語義層次的資訊先找出文件中專有名詞類型的重要項目詞彙,再以關聯樣版將文件中的關聯資訊萃取出來,提供了一套有效的從文字性資料中發掘關聯資訊的方法。最後在利用萃取後的中文文法規則,協助我們在概念圖上的建置。第二次的實驗則是比對了有關於傳統文字探勘與本研究上做法的差異,並針對不同的結果用比較表格的方式呈現了不同之處。
Novak proposed technique of Concept Mapping at Cornell University in the 1970s, as a way to increase meaningful learning in the sciences. Concept Map is a hierarchical structure [Novak, 2006]; it also has the level, which represents cognitive knowledge in user mind, the vector, which explains the sequence with scenario and the verbs, which describes the relationship between two concepts.
As past research, Concept Maps always painted by students using the hands with pencils and papers after the course and beginning the course teachers teaching students, how to paint it? However, students, who do not have perspective enough on the field, usually got the misconception [Rebich, 2005]. Many researchers using structural data to analyze the problem may help students building concepts on Website [Qiu,2003; Lee, 2004]. Because the teachers who given some concept nodes and links supported in system that helped students building concept network in a field. Some researchers use Text Mining [SanJuan, 2006; Feldman, 1998; Weiss, Weigend, 1999; Silverstein, 1998] to analysis semi-structure or non-structure documents, such as news [Montes, Gelbukh and López, 2001], chemical data [Liu, Navathe, Civera, Dasigi, Ram, Ciliax and Dingledine, 2005], patent data [Yoon and Park, 2004], and clinical medical records [Zhou, Han, Chankai, Prestrud, Brooks, 2006]. Text Mining also likes data mining talking about association rules [Wong, Whitney and Thomas, 1999; Berardi and Lapi, 2005], looking for strength relation between two terms ignoring some rare but important information.
In this paper purposed a prototype of Concept Map, we define some template coming from students’ article by using Chinese writing. Through each extracting steps introducing the Text Mining and Concept Map letting us understanding the concept nodes and the relationship between them, Natural Language Processing, searching in semantic network helping us looking for Chinese grammar pattern. After these processes, Chance discovery and Small World method can help us doing group discussion, let data between human and machine. Finally, through this method attain to help experts to make some decisions and hand a group discussion to get more creative or idea.
In experiment has gotten some results, first experiment introduce the parsing process and find the few rules including the proper noun, phrase rules, Chinese grammar rules, Conjunction rules and so on. Second experiment compares with text mining and gets some difference results that have been listed in the compare table.
TABLE OF CONTNETS I
LIST OF FIGURES III
LIST OF TABLES IV
CHAPTER 1. INTRODUCTION 1
1.1 BACKGROUND 1
1.2 CONTEXT OF THE PROBLEM 2
1.3 PURPOSE OF THE STUDY 4
CHAPTER 2. REVIEW OF THE LITERATURE 6
2.1 TEXT MINING 6
2.2 NATURAL LANGUAGE PROCESSING 8
2.3 CONCEPT MAPPING 10
2.4.2 DOUBLE HELICAL MODEL 13
2.5 SUMMARY 15
CHAPTER 3. METHODOLOGY 17
3.1 PARSING PROCESS 18
3.2 TEXT PREPROCESSING 24
3.3 SENTENCES MATCHING 25
3.4 FREQUENCY ANALYSIS 26
3.5 CONCEPT MAP CONSTRUCTION 27
CHAPTER 4. RESULTS 28
4.1 DATA DESCRIPTION 29
4.2 FIRST EXPERIMENT 30
4.3 SECOND EXPERIMENT 38
CHAPTER 5. CONCLUSION AND FUTURE WORK 42
5.1 CONCLUSION 42
5.2 FUTURE WORK 43
REFERENCES 45
APPENDIX 1.中研院平衡語料庫詞類標記集 53
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