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Fig. 3. Two passages with the same words but the 2"! passage contains some letters with diacritics (highlighted in green) and a substitution of some interchangeable letters (highlighted in yellow). A simple plagiarism detector may fail to match them. Regarding this aspect, Magooda et al. reported the use of two- language dependent processing in the source retrieval phase: stemming queries before submitting them to the search engine and extracting named entities. In the text alignment phase, words are stemmed in the skip-gram approach. Moreover, their methods pre- process the text by removing diacritics and normalizing letters)”. Alzahrani method is nearly language independent. The only reported language-specific process was stop words removal. It was applied as a pre-processing step on suspicious and source documents.

Figure 3 Two passages with the same words but the 2"! passage contains some letters with diacritics (highlighted in green) and a substitution of some interchangeable letters (highlighted in yellow). A simple plagiarism detector may fail to match them. Regarding this aspect, Magooda et al. reported the use of two- language dependent processing in the source retrieval phase: stemming queries before submitting them to the search engine and extracting named entities. In the text alignment phase, words are stemmed in the skip-gram approach. Moreover, their methods pre- process the text by removing diacritics and normalizing letters)”. Alzahrani method is nearly language independent. The only reported language-specific process was stop words removal. It was applied as a pre-processing step on suspicious and source documents.

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Table 1. Statistics on the external plagiarism detection training and test corpora.
Table 1. Statistics on the external plagiarism detection training and test corpora.
Table 3. Text alignment approaches used in participants methods.
Table 3. Text alignment approaches used in participants methods.
See [37] for more information on plagiarism detection evaluation measures. Table 4 provides the performance results of the participants’ methods as well as the baseline on the test corpus.
See [37] for more information on plagiarism detection evaluation measures. Table 4 provides the performance results of the participants’ methods as well as the baseline on the test corpus.
Table 5. Detailed performance of participant's methods. In each measure, the underlined values are the higher per parameter.
Table 5. Detailed performance of participant's methods. In each measure, the underlined values are the higher per parameter.
5.2 Method Description Table 6. Statistics on the intrinsic plagiarism detection training and test corpora.
5.2 Method Description Table 6. Statistics on the intrinsic plagiarism detection training and test corpora.
Fig. 5. Intrinsic plagiarism detection methods building block.
Fig. 5. Intrinsic plagiarism detection methods building block.
5.3.3 Detailed Results Table 8. Performance of the intrinsic plagiarism detection methods. —eeeoer NE NE I DE NIE EE Unlike the external approach, we think that the performance of the intrinsic approach could be influenced by the document length and the percentage of plagiarism it incorporates. Table 9 presents the performance of Mahgoub et al. and the baseline methods on the test corpus according to the aforementioned parameters in addition to the case length. The segmentation strategy of the baseline does not produce short chunks, therefore the precision is not computed in detected short cases. However, the actual short cases are detected with high recall. For both methods, the best performance is obtained in the medium cases, the short documents and the documents with much plagiarism. Nonetheless, since we have only two methods, we cannot generalize any observed pattern.
5.3.3 Detailed Results Table 8. Performance of the intrinsic plagiarism detection methods. —eeeoer NE NE I DE NIE EE Unlike the external approach, we think that the performance of the intrinsic approach could be influenced by the document length and the percentage of plagiarism it incorporates. Table 9 presents the performance of Mahgoub et al. and the baseline methods on the test corpus according to the aforementioned parameters in addition to the case length. The segmentation strategy of the baseline does not produce short chunks, therefore the precision is not computed in detected short cases. However, the actual short cases are detected with high recall. For both methods, the best performance is obtained in the medium cases, the short documents and the documents with much plagiarism. Nonetheless, since we have only two methods, we cannot generalize any observed pattern.
Related topics:
Information RetrievalNatural Language ProcessingPlagiarism DetectionPlagiarism studiesArabic NLPArabic Language NLP
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