TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
The Sequence Labeling Approach for Text Alignment of Plagiarism Detection |
| 영문제목(English Title) |
The Sequence Labeling Approach for Text Alignment of Plagiarism Detection |
| 저자(Author) |
Leilei Kong
Zhongyuan Han
Haoliang Qi
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| 원문수록처(Citation) |
VOL 13 NO. 09 PP. 4814 ~ 4832 (2019. 09) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Plagiarism detection is increasingly exploiting text alignment. Text alignment involves extracting the plagiarism passages in a pair of the suspicious document and its source document. The heuristics have achieved excellent performance in text alignment. However, the further improvements of the heuristic methods mainly depends more on the experiences of experts, which makes the heuristics lack of the abilities for continuous improvements. To address this problem, machine learning maybe a proper way. Considering the position relations and the context of text segments pairs, we formalize the text alignment task as a problem of sequence labeling, improving the current methods at the model level. Especially, this paper proposes to use the probabilistic graphical model to tag the observed sequence of pairs of text segments. Hence we present the sequence labeling approach for text alignment in plagiarism detection based on Conditional Random Fields. The proposed approach is evaluated on the PAN@CLEF 2012 artificial high obfuscation plagiarism corpus and the simulated paraphrase plagiarism corpus, and compared with the methods achieved the best performance in PAN@CLEF 2012, 2013 and 2014. Experimental results demonstrate that the proposed approach significantly outperforms the state of the art methods.
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| 키워드(Keyword) |
Plagiarism Detection
Text Alignment
Sequence Labeling
Probabilistic Graphical Model
Conditional Random Fields
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| 파일첨부 |
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