Meta Text Aligner: Text Alignment Based on Predicted Plagiarism Relation

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Text alignment is one of the main steps of plagiarism detection in textual environments. Considering the pattern in the distribution of the common semantic elements of the two given documents, different strategies may be suitable for this task. In this paper, we assume that the obfuscation level, i.e the plagiarism type, is a function of the distribution of the common elements in the two documents.

Based on this assumption, we propose META TEXT ALIGNER which predicts plagiarism relation of two given documents and employs the prediction results to select the best text alignment strategy. Thus, it will potentially perform better than the existing methods which use the same strategy for all cases. As indicated by the experiments, we have been able to classify document pairs based on plagiarism type with the precision of 89%.

Furthermore exploiting the predictions of the classifier for choosing the proper method or the optimal configuration for each type we have been able to improve the Plagdet score of the existing methods.

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