Uncovering highly obfuscated plagiarism cases using fuzzy semantic-based similarity model
Highly obfuscated plagiarism cases contain unseen and obfuscated texts, which pose difficulties when using existing plagiarism detection methods. A fuzzy semantic-based similarity model for uncovering obfuscated plagiarism is presented and compared with five state-of-the-art baselines. Semantic rela...
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| Format: | Article |
| Language: | English |
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Elsevier
2015-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157815000361 |
| _version_ | 1818012578994978816 |
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| author | Salha M. Alzahrani Naomie Salim Vasile Palade |
| author_facet | Salha M. Alzahrani Naomie Salim Vasile Palade |
| author_sort | Salha M. Alzahrani |
| collection | DOAJ |
| description | Highly obfuscated plagiarism cases contain unseen and obfuscated texts, which pose difficulties when using existing plagiarism detection methods. A fuzzy semantic-based similarity model for uncovering obfuscated plagiarism is presented and compared with five state-of-the-art baselines. Semantic relatedness between words is studied based on the part-of-speech (POS) tags and WordNet-based similarity measures. Fuzzy-based rules are introduced to assess the semantic distance between source and suspicious texts of short lengths, which implement the semantic relatedness between words as a membership function to a fuzzy set. In order to minimize the number of false positives and false negatives, a learning method that combines a permission threshold and a variation threshold is used to decide true plagiarism cases. The proposed model and the baselines are evaluated on 99,033 ground-truth annotated cases extracted from different datasets, including 11,621 (11.7%) handmade paraphrases, 54,815 (55.4%) artificial plagiarism cases, and 32,578 (32.9%) plagiarism-free cases. We conduct extensive experimental verifications, including the study of the effects of different segmentations schemes and parameter settings. Results are assessed using precision, recall, F-measure and granularity on stratified 10-fold cross-validation data. The statistical analysis using paired t-tests shows that the proposed approach is statistically significant in comparison with the baselines, which demonstrates the competence of fuzzy semantic-based model to detect plagiarism cases beyond the literal plagiarism. Additionally, the analysis of variance (ANOVA) statistical test shows the effectiveness of different segmentation schemes used with the proposed approach. |
| first_indexed | 2024-04-14T06:21:57Z |
| format | Article |
| id | doaj.art-e05720b229a74f72896633adca2582e4 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-14T06:21:57Z |
| publishDate | 2015-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-e05720b229a74f72896633adca2582e42022-12-22T02:07:59ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782015-07-0127324826810.1016/j.jksuci.2014.12.001Uncovering highly obfuscated plagiarism cases using fuzzy semantic-based similarity modelSalha M. Alzahrani0Naomie Salim1Vasile Palade2College of Computers and Information Technology (CIT), Taif University, Taif, Saudi ArabiaFaculty of Computer Science and Information Systems, University of Technology Malaysia, Johor, MalaysiaDepartment of Computer Science, University of Oxford, UKHighly obfuscated plagiarism cases contain unseen and obfuscated texts, which pose difficulties when using existing plagiarism detection methods. A fuzzy semantic-based similarity model for uncovering obfuscated plagiarism is presented and compared with five state-of-the-art baselines. Semantic relatedness between words is studied based on the part-of-speech (POS) tags and WordNet-based similarity measures. Fuzzy-based rules are introduced to assess the semantic distance between source and suspicious texts of short lengths, which implement the semantic relatedness between words as a membership function to a fuzzy set. In order to minimize the number of false positives and false negatives, a learning method that combines a permission threshold and a variation threshold is used to decide true plagiarism cases. The proposed model and the baselines are evaluated on 99,033 ground-truth annotated cases extracted from different datasets, including 11,621 (11.7%) handmade paraphrases, 54,815 (55.4%) artificial plagiarism cases, and 32,578 (32.9%) plagiarism-free cases. We conduct extensive experimental verifications, including the study of the effects of different segmentations schemes and parameter settings. Results are assessed using precision, recall, F-measure and granularity on stratified 10-fold cross-validation data. The statistical analysis using paired t-tests shows that the proposed approach is statistically significant in comparison with the baselines, which demonstrates the competence of fuzzy semantic-based model to detect plagiarism cases beyond the literal plagiarism. Additionally, the analysis of variance (ANOVA) statistical test shows the effectiveness of different segmentation schemes used with the proposed approach.http://www.sciencedirect.com/science/article/pii/S1319157815000361Feature extractionFuzzy similarityObfuscationPlagiarism detectionSemantic similarity |
| spellingShingle | Salha M. Alzahrani Naomie Salim Vasile Palade Uncovering highly obfuscated plagiarism cases using fuzzy semantic-based similarity model Journal of King Saud University: Computer and Information Sciences Feature extraction Fuzzy similarity Obfuscation Plagiarism detection Semantic similarity |
| title | Uncovering highly obfuscated plagiarism cases using fuzzy semantic-based similarity model |
| title_full | Uncovering highly obfuscated plagiarism cases using fuzzy semantic-based similarity model |
| title_fullStr | Uncovering highly obfuscated plagiarism cases using fuzzy semantic-based similarity model |
| title_full_unstemmed | Uncovering highly obfuscated plagiarism cases using fuzzy semantic-based similarity model |
| title_short | Uncovering highly obfuscated plagiarism cases using fuzzy semantic-based similarity model |
| title_sort | uncovering highly obfuscated plagiarism cases using fuzzy semantic based similarity model |
| topic | Feature extraction Fuzzy similarity Obfuscation Plagiarism detection Semantic similarity |
| url | http://www.sciencedirect.com/science/article/pii/S1319157815000361 |
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