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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhan Su Byung-Ryul Ahn Ki-Yol Eom Min-Koo Kang Jin-Pyung Kim Moon-Kyun Kim |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Artificial Intell., Sungkyunkwan Univ., Suwon (Zhan Su; Byung-Ryul Ahn; Ki-Yol Eom; Min-Koo Kang; Jin-Pyung Kim; Moon-Kyun Kim) |
| Abstract | Plagiarism in texts is issues of increasing concern to the academic community. Now most common text plagiarism occurs by making a variety of minor alterations that include the insertion, deletion, or substitution of words. Such simple changes, however, require excessive string comparisons. In this paper, we present a hybrid plagiarism detection method. We investigate the use of a diagonal line, which is derived from Levenshtein distance, and simplified Smith-Waterman algorithm that is a classical tool in the identification and quantification of local similarities in biological sequences, with a view to the application in the plagiarism detection. Our approach avoids globally involved string comparisons and considers psychological factors, which can yield significant speed-up by experiment results. Based on the results, we indicate the practicality of such improvement using Levenshtein distance and Smith-Waterman algorithm and to illustrate the efficiency gains. In the future, it would be interesting to explore appropriate heuristics in the area of text comparison. |
| Starting Page | 569 |
| Ending Page | 569 |
| File Size | 144158 |
| Page Count | 1 |
| File Format | |
| ISBN | 9780769531618 |
| DOI | 10.1109/ICICIC.2008.422 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-06-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Plagiarism Heuristic algorithms Distance measurement Biology Dynamic programming Arrays |
| Content Type | Text |
| Resource Type | Article |
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