An improved plagiarism detection scheme based on semantic role labeling

Plagiarism occurs when the content is copied without permission or citation. One of the contributing factors is that many text documents on the internet are easily copied and accessed. This paper introduces a plagiarism detection technique based on the Semantic Role Labeling (SRL). The technique analyses and compares text based on the semantic allocation for each term inside the sentence. SRL is superior in generating arguments for each sentence semantically. Weighting for each argument generated by SRL to study its behaviour is also introduced in this paper. It was found that not all arguments affect the plagiarism detection process. In addition, experimental results on PAN-PC-09 data sets showed that our method significantly outperforms the modern methods for plagiarism detection in terms of Recall, Precision and F-measure.

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► New plagiarism detection scheme based on Semantic Role Labeling (SRL).
► The plagiarized documents can be detected semantically.
► The proposed method were covered many of plagiarism types such as copy paste, paraphrasing, change the structure of plagiarized sentences and rewording using synonyms of the terms.
► Arguments behaviours were studied to select important arguments that can effect on plagiarism detection.
► Enhancement of similarity score by using Arguments Weighting Scheme.