進階搜尋


 
系統識別號 U0026-0812200914014835
論文名稱(中文) 以搜尋引擎進行剽竊模式之評估
論文名稱(英文) Assessing Plagiarism Patterns with Web Search Engines
校院名稱 成功大學
系所名稱(中) 工程科學系碩博士班
系所名稱(英) Department of Engineering Science
學年度 95
學期 2
出版年 96
研究生(中文) 劉奕廷
研究生(英文) Yi-Ting Liu
電子信箱 n9694122@mail.ncku.edu.tw
學號 n9694122
學位類別 碩士
語文別 英文
論文頁數 40頁
口試委員 指導教授-鄧維光
口試委員-王明習
口試委員-高宏宇
口試委員-戴碧如
中文關鍵字 剽竊偵測  軟體設計  搜尋引擎 
英文關鍵字 plagiarism detection  software design  search engine 
學科別分類
中文摘要 隨著資訊科技的日新月異,集結在網際網路上的資料量也以驚人的速度大幅增加。為解決大量資料的過載問題,人們通常使用搜尋引擎以作為一個能夠找到所需資訊的管道。然而,當搜尋引擎成為一個快速且有效的工具同時,剽竊者亦可輕易地藉此找到他人所撰寫的文字內容,並且經過重新組合後散佈這些文字內容,在此篇論文當中,我們開發了一個線上偵測系統,目的即是希望減少這種搜尋引擎的誤用行為。更明確地來說,當一篇可疑文章的內容被擷取出來後,會透過我們的系統以及搜尋引擎的合作來進行驗證。透過適當的設計,每一個被擷取出來的文字片段在以搜尋引擎驗證之前,都會給予不同的優先順序,如此一來在進行剽竊偵測時,我們可以減少許多不必要且重複性的運算浪費。經實驗研究之驗證,我們所提出的方法不論在理論上或實務上都顯示出它的效果和可行性。
英文摘要 As information technologies advance, the data amount gathered on the Internet increases at an incredible rapid speed. To solve the data overloading problem, people commonly use web search engines to find what they need. However, as search engines become an efficient and effective tool, plagiarists can grab, reassemble and redistribute text contents without much difficulty. In this thesis, we develop an online detection system to reduce such misapplication of search engines. Specifically, suspicious documents are extracted and verified through the collaboration of our plagiarism detection system and search engines. With a proper design, extracted text segments are given different priorities when sending them to search engines as the ascertainment of plagiarism. This greatly reduces unnecessary and repetitive works when performing plagiarism detection. Empirical study shows that the proposed approach is not only theoretically effective but also practically feasible.
論文目次 Chapter 1 Introduction 1
1.1 Motivation and Overview of the Thesis 1
1.2 Contributions of the Thesis 2
Chapter 2 Literature Survey 3
2.1 Use and Misuse of Web Search 3
2.2 Overview of the Plagiarism Problem 4
2.3 Software Tools for Plagiarism Detection 5
2.3.1 COPS 6
2.3.2 SCAM 7
2.3.3 SNTICH 8
2.3.4 Other Commercial Tools 8
2.4 Reusing Search Engine Results 9
Chapter 3 Developing an Online Plagiarism Detection System 11
3.1 System Flows for Online Plagiarism Detection 11
3.2 Schemes of Segment Ranking 13
3.3 Identification of Plagiarism Sources 16
Chapter 4 Empirical Studies 18
4.1 System Implementations 18
4.2 Testing Datasets 20
4.3 Experimental Results 25
4.3.1 Discussions of the Plagiarism Detection Process 25
4.3.2 Experiments on the Real Dataset 28
4.3.3 Experiments on the Synthetic Dataset 31
Chapter 5 Conclusions and Future Works 33
Bibliography 34
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