Analyzing Semantic Concept Paerns to Detect Academic Plagiarism

Detecting academic plagiarism is a pressing problem, e.g., for educational and research institutions, funding agencies, and academic publishers. Existing plagiarism detection systems reliably identify (nearly) copied text, but o‰en fail to detect disguised forms of academic plagiarism, such as paraphrases, translations, and idea plagiarism. We present Semantic Concept PaŠern Analysis an approach that performs an integrated analysis of semantic text relatedness and structural text similarity. Using 25 ocially retracted cases of academic plagiarism, we demonstrate that our approach can detect cases that established text matching approaches would not identify. We see the approach as a promising addition to improve the detection capabilities for strong paraphrases. We plan to further improve Semantic Concept PaŠern Analysis and include the approach as part of an integrated detection process that analyzes heterogeneous similarity features to beŠer identify the many possible forms of plagiarism in academic documents.