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系統識別號 U0026-0802201720335400
論文名稱(中文) 基於語意檢索代理人之案例推論系統的研發與應用
論文名稱(英文) Development and Application of Case-based Reasoning Systems based on Semantic Retrieval Agent
校院名稱 成功大學
系所名稱(中) 工程科學系
系所名稱(英) Department of Engineering Science
學年度 105
學期 1
出版年 106
研究生(中文) 張家瑋
研究生(英文) Jia-Wei Chang
學號 n98011013
學位類別 博士
語文別 英文
論文頁數 81頁
口試委員 指導教授-王宗一
口試委員-李明哲
口試委員-黃悅民
口試委員-林豪鏘
召集委員-孫光天
中文關鍵字 短文語意相似度  協同過濾  案例推論機制  推薦系統  智慧型數位學習系統 
英文關鍵字 Short-text Semantic Similarity  Collaborative Filtering  Case-based Reasoning  Recommender System  Intelligent e-Learning System 
學科別分類
中文摘要 本研究之目的在於研發一智慧型推薦系統幫助使用者在數位學習或電子商務等各種應用情境中提供更友善的服務。若推薦系統可以接受自然語言語句的輸入,便能讓使用者不必思考如何下達準確的關鍵字,而是允許其輸入一個模糊的概念也能找到符合或相似的候選項目。並且,如果該系統能依照概念的相似程度與協同過濾機制提供有效的排序,如此便可以減少使用者的搜尋疲勞並幫助他們獲取想要的東西。因此,本研究提出了一種基於案例的推理系統的新框架,包括協作過濾機制和基於語意的案例檢索智慧代理人。此外,該案例檢索智慧代理人整合了短文本語意相似性測量(STSS)和文本蘊涵辨識(RTE)等自然語言處理技術。為驗證該案例檢索智慧代理人的效能,本研究分別以STSS和RTE等作為任務目標來進行現有之方法與所提出的方法的比較。根據綜合評估的結果,本研究所提出的方法優於其他STSS與RTE方法。本研究以網站書店作為案例應用來驗證所提出之方法的有效性。根據該案例研究的結果,本研究所提出的基於語意檢索代理人的案例推論系統的確優於使用字串相似度方法的系統與一知名的網路書城系統。除此之外,本研究還提出了一個使用本語意分析技術於數位學習環境中的潛力應用。
英文摘要 This study aims to propose an intelligent recommender system to build a more user-friendly environment for various scenarios, such as Business-to-Consumer e-commerce or E-learning environments. Users can express their requirements in the form of a sentence or short-text rather than exact keywords if recommender system accepts natural language. Also, the recommender system might reduce the fatigue user experience and help them obtain what they want if it can provide an efficient ranking based on semantic similarity and collaborative filtering. This study presents a novel case-based reasoning framework that includes a collaborative filtering mechanism and a semantic-based case retrieval agent. In this study, we integrate the short-text semantic similarity (STSS) and recognizing textual entailment (RTE) into the case retrieval agent. This study compared with existing STSS and RTE methods to evaluate the performance of the proposed approach. According to the results, the proposed approach outperforms most previously described methods. Moreover, a case study of an online bookstore was conducted for investigating the effectiveness of the proposed approach. The results indicate that the proposed approach outperforms the system using string similarity and a famous e-commerce system. On the other hand, a potential application using semantic-based approach for e-learning environment was presented in another case study.
論文目次 摘要 i
Abstract ii
誌謝 iii
Table of content iv
List of figures vi
List of tables viii
Chapter 1 Introduction 1
Chapter 2 Literature Review 5
2.1 CBR Systems 5
2.2 Word-sense Disambiguation 8
2.3 Short-text/Sentence Semantic Similarity Measures 11
Chapter 3 Methodology 15
3.1 The Framework of the Proposed CBR System 16
3.2 Design of the Proposed Case Retrieval Agent 18
3.3 The design of the POS-based STSS Measure 20
Chapter 4 Performance Test of the Case Retrieval Agent 27
4.1 Experiments for STSS Task 28
4.2 Experiments for RTE Task 31
4.3 Conclusions for Performance Test of the Case Retrieval Agent 33
Chapter 5 Case Study: An Online Bookstore 35
5.1 The Case Study of an Online Bookstore 35
5.2 Experimental Design 38
5.3 Experimental Results of Natural Language Queries 40
5.4 Experimental Results of Keyword Queries 43
5.5 Discussions for the Case Study of an Online Bookstore 45
Chapter 6 Case Study: Chinese sentence learning system 48
6.1 Self-explanation-based Chinese sentence learning system 48
6.2 Experimental Design for the Pilot Study 54
6.3 Experimental Results for the Pilot Study 56
6.4 Further Analysis 59
6.5 Discussion for the Pilot Study 65
6.6 Future Work for the Pilot Study 67
Chapter 7 Conclusions 69
Appendix A. The benchmark of thirty sentence pairs for STSS task 71
Acknowledgements 74
Reference 75
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