一個偵測跨語言內容相互引用的系統｜國立清華大學博碩士論文庫

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研究生：	鞏和平 Jose P. Gonzalez-Brenes
論文名稱：	一個偵測跨語言內容相互引用的系統 SMURF: A Cross-lingual Co-derivative Detection System
指導教授：	林福仁 Fu-Ren Lin
口試委員:
學位類別：	碩士 Master
系所名稱：	科技管理學院 - 科技管理研究所 Institute of Technology Management
論文出版年：	2007
畢業學年度：	95
語文別：	英文
論文頁數：	46
中文關鍵詞：	multilingual plagarism detection 、cross-lingual co-derivative detection 、translation detection
外文關鍵詞：	multilingual plagarism detection, cross-lingual co-derivative detection, translation detection
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An automatic approach to detect content overlapping will mitigate the workload on the repetitiveness and tedious nature of manually checking the originality of a large pool of documents. The objective of this research is to design and evaluate a novel algorithm, SMURF –Semantic MUltilingual Related-Document Finder, aimed to find pairs of documents in different languages that share a common source (co-derivative) which may be used to facilitate the protection of intellectual property. We demonstrate SMURF on identifying English co-derivatives on the Web of Spanish documents on several textual domains with a sentence-level precision of 88.75%. Although SMURF’s design focused
on English and Spanish, the concepts applied could be easily implemented on other languages where the constituent technologies have been studied.

iv
Table of Contents
ABSTRACT ........................................................................................................................ ii
Acknowledgment ............................................................................................................... iii
List of Tables ..................................................................................................................... vi
List of Figures ................................................................................................................... vii
Chapter 1 Introduction .........................................................................................................1
Chapter 2 Literature Review ................................................................................................4
2.1 Constituent Technologies ..................................................................................... 4
(1) Sentence Boundary Detection ........................................................................... 4
(2) Bilingual Sentence Alignment .......................................................................... 5
(3) Bilingual Word Alignment ............................................................................... 5
(4) Part of Speech Tagging ..................................................................................... 6
(5) Key-phrase Selection ........................................................................................ 6
2.2 Previous Efforts .................................................................................................... 7
(1) SNITCH: Copy-Paste Detection ....................................................................... 8
(2) Sherlock: Sentence Based Plagiarism Detection .............................................. 9
Chapter 3 Research Framework .........................................................................................10
3.1 Overview ............................................................................................................ 10
3.2 Interface .............................................................................................................. 11
3.3 Architecture of SMURF ..................................................................................... 12
(1) Tokenization ................................................................................................... 13
(2) Translation ...................................................................................................... 15
(3) Key-phrase extraction ..................................................................................... 18
(4) Key-phrase translation .................................................................................... 18
(5) Document Search ............................................................................................ 19
(6) Clean search results ......................................................................................... 19
(7) Co-derivative identification ............................................................................ 20
(8) Parsing of results ............................................................................................. 21
Chapter 4 Evaluation..........................................................................................................22
4.1 Experimental Design .......................................................................................... 22
4.2 Testing Data ....................................................................................................... 23
4.3 Evaluation results and discussion ....................................................................... 24
v
(1) Experiment 1 ................................................................................................... 24
(2) Experiment 2 ................................................................................................... 25
Chapter 5 Conclusions and Future Work ...........................................................................31
References ..........................................................................................................................32
Appendix A Cross Document Relationship (Source: Radev, n.d) .....................................34
Appendix B Data Set .........................................................................................................37
B.1 Enigma Original English Source ....................................................................... 37
B.2 Enigma Translation............................................................................................ 38
B.3 Space Elevator English Source ......................................................................... 39
B.4 Space Elevator Spanish Translation .................................................................. 39
B.5 Salvador Dal? English Source ........................................................................... 40
B.6 Salvador Dal? Spanish Translation .................................................................... 41
B.7 Yesterday (Song) English Source ...................................................................... 41
B.8 Yesterday (Song) Spanish Translation ............................................................... 42
B.9 Supreme Court of the United States English Source ......................................... 42
B.10 Supreme Court of the United States Spanish Translation................................ 43
B.11 Tony Blair English Source ............................................................................... 45
B.12 Tony Blair Spanish Translation ....................................................................... 46
vi
List of Tables
Table 2.1 Mean of acceptable key terms extracted ........................................................ 6
Table 2.2 Comparison of commercial plagiarism detection software ........................... 8
Table 4.1 Translations Detected .................................................................................. 24
Table 4.2 Precision of the sentence-level alignments found by SMURF .................... 26
Table 4.3 Sentences incorrectly identified as co-derivatives by SMURF ................... 27
Table 4.4 Co-derivatives sentences detected with Dice Score below 55% ................. 28
Table 4.5 Co-derivatives example found for Tony Blair document ............................ 29
Table 4.6 The different kinds of the co-derivatives found ........................................... 30
vii
List of Figures
Figure 2.1 Example of a non-trivial sentence alignment ............................................... 5
Figure 2.2 All the possible alignments of Chinese-English parallel sentences ............. 6
Figure 2.3 SNITCH Algorithm ...................................................................................... 9
Figure 3.1 Architecture of SMURF ............................................................................ 10
Figure 3.2 Upload Spanish document .......................................................................... 11
Figure 3.3 Automatic key-phrases detection ............................................................... 11
Figure 3.4 Co-derivative annotations on the Spanish document ................................. 12
Figure 3.5 Sample Spanish text ................................................................................... 12
Figure 3.6 Pseudo-code of the find co-derivative function ......................................... 13
Figure 3.7 Example of grouping words with a sliding window ................................... 14
Figure 3.8 Example of a Spanish tokenization ............................................................ 15
Figure 3.9 Translation algorithm example ................................................................... 16
Figure 3.10 Sample translation .................................................................................... 17
Figure 3.11 Sample n-grams extracted ....................................................................... 18
Figure 3.12 Example of translation of “illegal copy” key-phrase ............................... 19
Figure 4.1 Workflow for Experiment 1 ....................................................................... 22
Figure 4.2 Proportion of the relation type annotated of the co-derivatives found ....... 30

                                

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