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中文論文名稱 以互動式中文回饋機制建置的自學系統
英文論文名稱 A Chinese Interactive Feedback Mechanism for a Self-Learning System
校院名稱 淡江大學
系所名稱(中) 資訊工程學系博士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 95
學期 1
出版年 96
研究生中文姓名 簡志宇
研究生英文姓名 Chih-Yu Jian
學號 890190118
學位類別 博士
語文別 英文
口試日期 2007-01-04
論文頁數 74頁
口試委員 指導教授-陳瑞發
委員-莊淇銘
委員-林偉川
委員-王英宏
委員-葛煥昭
委員-陳瑞發
中文關鍵字 自然語言  切詞方法  文法  互動回饋  語彙資料庫 
英文關鍵字 natural language  segmentation method  grammar  interactive feedback  lexical database 
學科別分類 學科別應用科學資訊工程
中文摘要 隨著網際網路的廣泛使用,線上互動式自學系統可讓使用者以自己的喜好進行學習,但要讓電腦系統瞭解人類使用的自然語言還是有其困難所在,尤其是中文,首先,中文裡面每個字詞之間並沒有任何區隔符號,第二是因為口語中所使用的句型未必符合正規的文法規則,使得難以去剖析及瞭解使用者真正的語意,第三是要把應用範圍限制在一個特定的領域,否則將太過複雜而難以分析。本論文提出一個互動式回饋機制的自學系統,能剖析並瞭解使用者輸入的自然語言,進而瞭解使用者要學習的內容,對不同的使用者提供客制化的回應內容。
英文摘要 Considering the popularity of the Internet, an automatic interactive feedback system for self-learning websites is becoming increasingly desirable. However, computers still have problems understanding natural languages, especially the Chinese language, firstly because the Chinese language has no space to segment lexical entries (its segmentation method is more difficult than that of English) and secondly because of the lack of a complete grammar in the Chinese language, making parsing more difficult and complicated. Building an automated Chinese feedback system for special application domains could solve these problems. This thesis proposes an interactive feedback mechanism in a self-learning website that can parse, understand and respond to Chinese sentences. This mechanism utilizes a specific lexical database according to the particular application. In this way, self-learning websites can implement a special application domain that chooses the proper response in a user friendly, accurate, and timely manner.
論文目次 Contents

Chapter1 Introduction……………………………………………….....1

Chapter2 Review of Related Works…………………………………....4
2.1 Segmentation methods……………………………………………4
2.1.1 Method of Regular Segmentation…………………………..4
2.1.2 Method of Statistically Segmenting Sentences.....................5
2.1.3 Mixed Method of Segmenting Sentence………………...…6
2.1.4 Segmenting Method of Genetic Algorithms (Gas)…………6
2.2 Grammar Analysis………………………………………………..7
2.2.1 Context Free Grammar (CFG)……………………………...7
2.2.2 Slot and Filler………………………………………………9
2.2.3 Link Grammar Technology………………………………...9
2.3 Memory-based Parsing System…………………………………12
2.4 Bayesian Network..…………………………………………..…14

Chapter3 Overview of the Proposed Architecture…………………..16

Chapter4 Segmentation System………………………………………19
4.1 Separation……...………………………………………………..21
4.2 Corpora-comparing system……………………………………...23
4.3 Unknown Word Judgment System……………………………...25
4.4 The Data Structure of the Segmentation Tree Node…………….26
4.5 Context-proofreading System…………………………………...28
4.6 Weighted-calculation System……………………………….......30
4.7 Process of Segmentation System………………………………..31

Chapter5 Syntactic Analysis System…………………………………34 5.1 Word-based Link Grammar……………………………………..35
5.2 Fault-tolerance Mechanism……………………………………..38
5.3 Process of Syntactic Analysis System…………………………..39

Chapter6 Semantic Analysis System…………………………………44
6.1 Memory-based parsing system………………………………….46
6.1.1 Concept Sequence Layer…………………………………46
6.1.2 Semantic Concept Hierarchy……………………………..48
6.1.3 Instance Layer…………………………………………..…49
6.2 Learning Mechanism of Semantics………………………….….50
6.2.1 Generalization..……………………………………………50
6.2.2 Specialization……………………………………………..52
6.3 Semantic Network……………………………………………....53
6.4 Process of Semantic Analysis System…………………………54

Chapter7 Response System……………………………………………56
7.1 Searching goal…...……………………………………………...56
7.1.1 Existence of Searching goal……………………….……..58
7.1.2 Knowledge domain of Searching goal…………………….63
7.2 Knowledge domain….........…………………………………...65
7.3 Teaching Material Difficulty…..……………………………...66

Chapter8 Conclusion and Future Work…………..………………….68

References…………………………………………………………….70
Table of Figures
Fig. 1 CFG grammar analysis…………………………………………….8
Fig. 2 Words and its plug……………………………….………………10
Fig. 3 Words and connectors in the dictionary………………………..10
Fig. 4 The simplified form of Fig. 2 & 3……………………………..11
Fig. 5 Part of knowledge base used for processing : “The Shining
Path”………………………….…………………………………………13
Fig. 6 An example of BN……………………………………………….14
Fig. 7 Flowchart of feedback system…………………………………..17
Fig. 8 The architecture of the segmentation system……………………19
Fig. 9 Separation system’s flow chart…………….………………….…22
Fig. 10 Unknown word judgment system process…………………….25
Fig. 11 segmentation tree structure…………………………………..…26
Fig. 12 node data structure…………………………………………...…27
Fig. 13 The flow chart of the keyword in context comparing system…..29
Fig. 14 List of separating from the first layer to the fourth layer……..32
Fig. 15 Flowchart of the syntactic analysis system……………………35
Fig. 16 Fault-tolerance processing with omitting of the preposition……39
Fig. 17 Process of syntactic analysis……………………………………42
Fig. 18 Result of the syntactic analysis…………………………………43
Fig. 19 Flowchart of the semantic analysis system………………..……44
Fig. 20 Structure of the concept sequence layer………………………47
Fig. 21 Structure of semantic concept hierarchy………………………49
Fig. 22 Example of semantic verification……………………………….50
Fig. 23 Generalization……………………………………………..……51
Fig. 24 Specialization……………………………………………….......52
Fig. 25 An example of semantic network………………………………54
Fig. 26 Semantic network of example sentence…...……………………55
Fig. 27 Belief network of existence……………………………………..58
Fig. 28 Trained topology of BN and keyword groups………………..…60
Fig. 29 Belief network of knowledge domain…………………….….…64
Fig. 30 Bayesian Network of Knowledge domain………………….......65
Fig. 31 Bayesian Network of Teaching Material Difficulty………….....66
IV
Table of Tables
Table 1: The words and linking requirements in a dictionary…………11
Table 2: corpora data structure……………………………..…………. .23
Table 3: The segmentation table of sentence…………………………....33
Table 4: The linking rules of each part of speech…………………….…35
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