External and Intrinsic Plagiarism Detection Using Vector Space Models
Markus Muhr, Mario Zechner, Roman Kern, Michael Granitzer · 2009
Plagiarism detection can be divided in external and intrinsic methods. Naive external plagiarism analysis suffers from computationally demanding full near- est neighbor searches within a reference corpus. We present a conceptually simple space partitioning approach to achieve search times sub linear in the number of ref- erence documents, trading precision for speed. We focus on full duplicate searches while achieving acceptable results in the near duplicate case. Intrinsic plagiarism analysis tries to find plagiarized passages within a document without any exter- nal knowledge. We use several topic independent stylometric features from which a vector space model for each sentence of a suspicious document is constructed. Plagiarized passages are detected by an outlier analysis relative to the document mean vector. Our system was created for the first PAN competition on plagiarism detection in 2009. The evaluation was performed on the challenge's development