A Ranking-Based Text Matching Approach for Plagiarism Detection

Leilei KONG  Zhongyuan HAN  Haoliang QI  Zhimao LU  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E101-A   No.5   pp.799-810
Publication Date: 2018/05/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E101.A.799
Type of Manuscript: PAPER
Category: Information Theory
plagiarism detection,  plagiarism text matching,  high-obfuscation plagiarism,  ranking,  meteor,  

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This paper addresses the issue of text matching for plagiarism detection. This task aims at identifying the matching plagiarism segments in a pair of suspicious document and its plagiarism source document. All the time, heuristic-based methods are mainly utilized to resolve this problem. But the heuristics rely on the experts' experiences and fail to integrate more features to detect the high obfuscation plagiarism matches. In this paper, a statistical machine learning approach, named the Ranking-based Text Matching Approach for Plagiarism Detection, is proposed to deal with the issues of high obfuscation plagiarism detection. The plagiarism text matching is formalized as a ranking problem, and a pairwise learning to rank algorithm is exploited to identify the most probable plagiarism matches for a given suspicious segment. Especially, the Meteor evaluation metrics of machine translation are subsumed by the proposed method to capture the lexical and semantic text similarity. The proposed method is evaluated on PAN12 and PAN13 text alignment corpus of plagiarism detection and compared to the methods achieved the best performance in PAN12, PAN13 and PAN14. Experimental results demonstrate that the proposed method achieves statistically significantly better performance than the baseline methods in all twelve document collections belonging to five different plagiarism categories. Especially at the PAN12 Artificial-high Obfuscation sub-corpus and PAN13 Summary Obfuscation plagiarism sub-corpus, the main evaluation metrics PlagDet of the proposed method are even 22% and 43% relative improvements than the baselines. Moreover, the efficiency of the proposed method is also better than that of baseline methods.