A Ranking Approach to Source Retrieval of Plagiarism Detection

• DOI: 10.1587/transinf.2016edl8090
• Publication Date: 2016-12-31T17:21:56Z
• Authors: Leilei KONG; Zhimao LU; Zhongyuan HAN; Haoliang QI
• Keywords: Clef; Plagiarism detection; Baseline (sea); Learning to Rank; Ranking SVM; Rank (graph theory)

This paper addresses the issue of source retrieval in plagiarism detection. The task is retrieving all plagiarized sources a suspicious document from corpus whilst minimizing costs. classification-based methods achieved best performance current researches retrieval. points out that it more important to cast problem as ranking and employ learning rank perform Specially, employs RankBoost Ranking SVM obtain candidate documents. Experimental results on dataset PAN@CLEF 2013 Source Retrieval show based significantly outperforms baseline classification. We argue considering better than classification problem.