A graph based automatic plagiarism detection technique to handle artificial word reordering and paraphrasing

Abstract

Most of the plagiarism detection techniques are based on either string based matching or semantic matching of adjacent strings. However, due to the use of artificial word re-ordering and paraphrasing, the detection of plagiarism has become a challenging task of significant interest. To solve this issue, we concentrate on identification of overlapping adjacent plagiarized word patterns and overlapping non-adjacent/reordered plagiarized word patterns from target document(s). Here the main aim is to capture the simple cases and the complex cases (i.e., artificial word reordering and/or paraphrasing) of plagiarism in the target document. For this first of all we identify the relation between all overlapping word pairs with the help of controlled closeness centrality and semantic similarity. Next, to extract the plagiarized word patterns, we introduce the use of minimum weighted bipartite clique covers. We use the plagiarized word patterns in the identification of plagiarized texts from the target document. Our experimental results on publicly available and annotated dataset like: 'PAN 2012 plagiarism detection dataset' and 'Student answer related plagiarism dataset' shows that it performs better than state-of-arts systems in this area. © 2014 Springer-Verlag Berlin Heidelberg.