Skip to Main Content
Existing anti-plagiarism tools are, in fact, text matching systems but do not make accurate judgments about plagiarism. Texts that are acceptable to be redundant and texts that are cited properly are all highlighted as plagiarism, and the real decision of plagiarism is left up to the user. To reduce the human input and to give more reliance to automatic plagiarism detectors, we propose an Intelligent Plagiarism Reasoner (iPlag), which works by combining several analytical procedures. Scholarly documents under investigation are segmented into logical tree-structured representation using a procedure called D-SEGMENT. Statistical methods are utilised to assign numerical weights to structural components under a technique called C-WEIGHT. Relevance ranking (R-RANK) and plagiarism screening approaches (P-SCREEN) are adjusted to incorporate structural weights, citation evidences, syntax-based and semantic-based methods into plagiarism detection results. We encourage current plagiarism detection systems to adapt the proposed framework.