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A Ranking Approach to Source Retrieval of Plagiarism Detection
Leilei KONG Zhimao LU Zhongyuan HAN Haoliang QI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2017/01/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Data Engineering, Web Information Systems
plagiarism detection, source retrieval, learning to rank, classification,
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This paper addresses the issue of source retrieval in plagiarism detection. The task of source retrieval is retrieving all plagiarized sources of a suspicious document from a source document corpus whilst minimizing retrieval costs. The classification-based methods achieved the best performance in the current researches of source retrieval. This paper points out that it is more important to cast the problem as ranking and employ learning to rank methods to perform source retrieval. Specially, it employs RankBoost and Ranking SVM to obtain the candidate plagiarism source documents. Experimental results on the dataset of PAN@CLEF 2013 Source Retrieval show that the ranking based methods significantly outperforms the baseline methods based on classification. We argue that considering the source retrieval as a ranking problem is better than a classification problem.