Meta Text Aligner: Text Alignment Based on Predicted Plagiarism Relation

Our paper "Meta Text Aligner: Text Alignment Based on Predicted Plagiarism Relation", with Samira Abnar, and Azadeh Shakery, has been accepted as a short paper at Conference and Labs of the Evaluation Forum (CLEF'15). \o/


Text alignment is one of the main steps of plagiarism detection in textual environments. Considering the pattern in 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.

For more details, please read the following paper:


Our paper was nominated for the CLEF2015 "Best Short Paper" award! 🙂

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