Semantic-Based Integrated Plagiarism Detection Approach for English Documents

REFERENCES

• I. Ahmad, “How much data is generated every minute?[infographic],” Retrieved September, 2020, Available: https://www.socialmediatoday.com/news/hmuch-data-is-generated-every-minute-infographic-1/525692/.
   Google Scholar
• Merriam-Webster. “Plagiarize.” In Merriam-Webster.com dictionary. Retrieved September 26, 2020, Available: https://www.merriam-webster.com/dictionary/plagiarize. n.d.
   Google Scholar
• D. Gupta, “Study on extrinsic text plagiarism detection techniques and tools,” J. Eng. Sci. Technol. Rev., Vol. 9, no. 5, 1 Sep. 2016. DOI:10.25103/jestr.135.04
   Google Scholar
• M. Elamine, F. Bougares, S. Mechti, and L. H. Belguith, “Extrinsic plagiarism detection for French language with word embeddings,” in International Conference on Intelligent Systems Design and Applications, Springer, Cham, 3 Dec. 2019, pp. 217–224.
   Google Scholar
• M. AlSallal, R. Iqbal, V. Palade, S. Amin, and V. Chang, “An integrated approach for intrinsic plagiarism detection,” Future Gener. Comput. Syst., Vol. 96, pp. 700–712, Jul. 2019. DOI:10.1016/j.future.2017.11.023
   Web of Science ®Google Scholar
• I. Bensalem, P. Rosso, and S. Chikhi, “On the use of character n-grams as the only intrinsic evidence of plagiarism,” Lang. Resour. Eval., Vol. 53, no. 3, pp. 363–396, Sep. 2019. DOI:10.1007/s10579-019-09444-w
   Web of Science ®Google Scholar
• PAN-PC-2011 datasets. Available: https://pan.webis.de/data.
   Google Scholar
• M. Potthast, M. Hagen, T. Gollub, M. Tippmann, J. Kiesel, P. Rosso, and B. Stein, “Overview of the 5th international competition on plagiarism detection,” in CLEF Conference on Multilingual and Multimodal Information Access Evaluation, CELCT, 2013, pp. 301–331.
   Google Scholar
• A. Barrón-Cedeño, and P. Rosso, “On automatic plagiarism detection based on n-grams comparison,” in European Conference on Information Retrieval, Springer, Berlin, Heidelberg, Apr. 2009, pp. 696–700.
   Google Scholar
• E. Stamatatos, “Plagiarism detection using stopword n-grams,” J. Am. Soc. Inf. Sci. Technol., Vol. 62, no. 12, pp. 2512–2527, Dec. 2011. DOI:10.1002/asi.21630
   Web of Science ®Google Scholar
• P. Shrestha, and T. Solorio, “Using a variety of n-grams for the detection of different kinds of plagiarism,” in Notebook for PAN at CLEF 2013, 2013.
   Google Scholar
• D. Gupta, K. Vani, and L. M. Leema, “Plagiarism detection in text documents using sentence bounded stop word n-grams,” J. Eng. Sci. Technol., Vol. 11, no. 10, pp. 1403–1420, Oct. 2016.
   Web of Science ®Google Scholar
• M. Wielgosz, P. Russek, E. Jamro, and K. Wiatr, “Evaluation and implementation of n-gram-based algorithm for fast text comparison,” Comput. Inform., Vol. 36, no. 4, pp. 887–907, Nov. 2017.
   Web of Science ®Google Scholar
• D. Leman, M. Rahman, F. Ikorasaki, B. S. Riza, and M. B. Akbbar, “Rabin karp and Winnowing algorithm for Statistics of text document plagiarism detection,” in 2019 7th International Conference on Cyber and IT Service Management (CITSM), IEEE, Nov. 2019, Vol. 7, pp. 1–5.
   Google Scholar
• S. Schleimer, D. S. Wilkerson, and A. Aiken, “Winnowing: local algorithms for document fingerprinting,” in Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, Jun. 2003, pp. 76–85.
   Google Scholar
• J. Grman, and R. Ravas, “Improved implementation for finding text similarities in large collections of data,” in Notebook Papers of CLEF 2011 LABs and Workshops, 2011.
   Google Scholar
• C. Grozea, and M. Popesc, “The encoplot similarity measure for automatic detection of plagiarism notebook for PAN at CLEF 2011,” 2011.
   Google Scholar
• A. H. Osman, N. Salim, M. S. Binwahlan, R. Alteeb, and A. Abuobieda, “An improved plagiarism detection scheme based on semantic role labeling,” Appl. Soft. Comput., Vol. 12, no. 5, pp. 1493–1502, May 2012. DOI:10.1016/j.asoc.2011.12.021
   Web of Science ®Google Scholar
• A. Ekbal, S. Saha, and G. Choudhary, “Plagiarism detection in text using vector space model,” in 2012 12th International Conference on Hybrid Intelligent Systems (HIS), IEEE, Dec. 2012, pp.366-371.
   Google Scholar
• V. K. Singh, and V. K. Singh, “Vector space model: an information retrieval system,” Int. J. Adv. Engg. Res. Studies/IV/II/, Vol. 4, no. 2, pp. 141–143, Jan. 2015.
   Google Scholar
• M. Paul, and S. Jamal, “An improved SRL based plagiarism detection technique using sentence ranking,” Procedia. Comput. Sci., Vol. 46, pp. 223–230, Jan. 2015. DOI:10.1016/j.procs.2015.02.015
   Google Scholar
• P. Jaccard, “The distribution of the flora in the alpine zone,” New Phytol., Vol. 11, no. 2, pp. 37–50, Feb. 1912. DOI:10.1111/j.1469-8137.1912.tb05611.x
   Google Scholar
• F. Rahutomo, T. Kitasuka, and M. Aritsugi, “Semantic cosine similarity,” 7th Int. Stud. Conf. Adv. Sci. Technol. ICAST, Vol. 4, no. 1, pp. 1, Oct. 2012.
   Google Scholar
• S. M. Alzahrani, N. Salim, and V. Palade, “Uncovering highly obfuscated plagiarism cases using fuzzy semantic-based similarity model,” J. King Saud Univ.-Comput. Inf. Sci., Vol. 27, no. 3, pp. 248–268, Jul. 2015. DOI:10.1016/j.jksuci.2014.12.001
   Google Scholar
• Z. Wu, and M. Palmer. “Verb semantics and lexical selection,” arXiv preprint cmp-lg/9406033, Jun. 1994.
   Google Scholar
• A. Abdi, N. Idris, R. M. Alguliyev, and R. M. Aliguliyev, “PDLK: plagiarism detection using linguistic knowledge,” Expert. Syst. Appl., Vol. 42, no. 22, pp. 8936–8946, Dec. 2015. DOI:10.1016/j.eswa.2015.07.048
   Web of Science ®Google Scholar
• D. Lin, “An information-theoretic definition of similarity,” InIcml, Vol. 98, no. 1998, pp. 296–304, Jul. 1998.
   Google Scholar
• A. Abdi, S. M. Shamsuddin, N. Idris, R. M. Alguliyev, and R. M. Aliguliyev, “A linguistic treatment for automatic external plagiarism detection,” Knowl. Based. Syst., Vol. 135, pp. 135–146, Nov. 2017. DOI:10.1016/j.knosys.2017.08.008
   Web of Science ®Google Scholar
• K. Vani, and D. Gupta, “Using K-means cluster based techniques in external plagiarism detection,” in 2014 International Conference on Contemporary Computing and Informatics (IC3I), IEEE, Nov. 2014 pp. 1268–1273.
   Google Scholar
• R. L. Cilibrasi, and P. M. Vitanyi, “The google similarity distance,” IEEE Trans. Knowl. Data Eng., Vol. 19, no. 3, pp. 370–383, 2007. DOI:10.1109/TKDE.2007.48
   Web of Science ®Google Scholar
• R. M. Aliguliyev, “A new sentence similarity measure and sentence based extractive technique for automatic text summarization,” Expert. Syst. Appl., Vol. 36, no. 4, pp. 7764–7772, 2009. DOI:10.1016/j.eswa.2008.11.022
   Web of Science ®Google Scholar
• A. Brlek, P. Franjic, and N. Uzelac, “Plagiarism detection using word2vec model,” Text Anal. Retr., Vol. 4, pp. 4–7, 2016.
   Google Scholar
• M. Sahi, and V. Gupta, “A novel technique for detecting plagiarism in documents exploiting information sources,” Cognit. Comput., Vol. 9, no. 6, pp. 852–867, Dec. 2017. DOI:10.1007/s12559-017-9502-4
   Web of Science ®Google Scholar
• L. Ahuja, V. Gupta, and R. Kumar, “A new Hybrid technique for detection of plagiarism from text documents,” Arab. J. Sci. Eng., Vol. 45, no. 12, pp. 1–4, May 2020.
   Web of Science ®Google Scholar
• R. Rada, H. Mili, E. Bicknell, and M. Blettner, “Development and application of a metric on semantic nets,” IEEE Trans. Syst. Man Cybern., Vol. 19, no. 1, pp. 17–30, 1989. DOI:10.1109/21.24528
   Web of Science ®Google Scholar
• A. Daud, J. A. Khan, J. A. Nasir, R. A. Abbasi, N. R. Aljohani, and J. S. Alowibdi, “Latent dirichlet allocation and POS tags based method for external plagiarism detection: LDA and POS tags based plagiarism detection,” International Journal on Semantic Web and Information Systems, Vol. 14, no. 3, pp. 53–69, 2018.
   Web of Science ®Google Scholar
• D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res., Vol. 3, pp. 993–1022, Mar. 2003.
   Web of Science ®Google Scholar
• K. Vani, and D. Gupta, “Unmasking text plagiarism using syntactic-semantic based natural language processing techniques: comparisons, analysis and challenges,” Inf. Process. Manag., Vol. 54, no. 3, pp. 408–432, May 2018. DOI:10.1016/j.ipm.2018.01.008
   Web of Science ®Google Scholar
• E. Gharavi, H. Veisi, and P. Rosso, “Scalable and language-independent embedding-based approach for plagiarism detection considering obfuscation type: no training phase,” Neural Computing and Applications, Vol. 7, pp. 1–5, Nov.2019.
   Google Scholar
• D. Bollegala, Y. Matsuo, and M. Ishizuka, “A relational model of semantic similarity between words using automatically extracted lexical pattern clusters from the web,” in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, ACL, Singapore, Aug. 2009, pp. 803–812.
   Google Scholar
• G. Zhu, and C. A. Iglesias, “Computing semantic similarity of concepts in knowledge graphs,” IEEE Trans. Knowl. Data Eng., Vol. 29, no. 1, pp. 72–85, 2016. DOI:10.1109/TKDE.2016.2610428
   Web of Science ®Google Scholar
• G. A. Miller, “Wordnet: a lexical database for English,” Commun. ACM, Vol. 38, no. 11, pp. 39–41, Nov. 1995. DOI:10.1145/219717.219748
   Web of Science ®Google Scholar
• W. N. Francis, and H. Kucera, “Brown corpus manual,” Lett. Ed., Vol. 5, no. 2, pp. 7, Jul. 1979.
   Google Scholar
• E. Loper, and S. Bird. “Nltk: The natural language toolkit,” arXiv preprint cs/0205028, May 2002.
   Google Scholar
• N. Hardeniya, J. Perkins, D. Chopra, N. Joshi, and I. Mathur. Natural language processing: python and NLTK. UK: Packt Publishing Ltd, Nov. 2016.
   Google Scholar
• A. Tomasic, and H. Garcia-Molina, “Query processing and inverted indices in shared-nothing text document information retrieval systems,” VLDB. J., Vol. 2, no. 3, pp. 243–275, Jul. 1993. DOI:10.1007/BF01228671
   Google Scholar
• Gene Ontology Consortium, “The Gene Ontology (GO) database and informatics resource,” Nucleic Acids Res., Vol. 32, no. suppl_1, pp. D258–D261, Jan. 2004.
   PubMedGoogle Scholar
• P. Altheide, “Spatial Data Transfer Standard (SDTS),” 1087–1095, 2008.
   Google Scholar
• C. Yang, Y. Zhu, M. Zhong, and R. Li, “Semantic similarity computation in knowledge graphs: comparisons and improvements,” in 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), Apr. 2019, pp. 249–252, IEEE.
   Google Scholar
• M. K. Prasad, and P. Sharma, “Combining common words and semantic features for sentence similarity,” in 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, Jul. 2018, pp.1–4.
   Google Scholar
• M. Potthast, B. Stein, A. Barrón-Cedeño, and P. Rosso, “An evaluation framework for plagiarism detection",  In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, Beijing-China, Aug. 2010, pp. 997–1005.
   Google Scholar
• M. Potthast, M. Hagen, A. Beyer, M. Busse, M. Tippmann, and B. Stein, “Overview of the 6th international competition on plagiarism detection,” in CLEF 2014 evaluation labs and Workshop – working notes papers. CEUR Workshop proceedings, L. Cappellato, N. Ferro, M. Halvey, and W. Kraaij, Ed. Sheffield-UK: CLEF and CEUR-WS, 2014, pp. 845–876.
   Google Scholar
• E. S. El-Alfy, R. E. Abdel-Aal, W. G. Al-Khatib, and F. Alvi, “Boosting paraphrase detection through textual similarity metrics with abductive networks,” Appl. Soft. Comput., Vol. 26, pp. 444–453, Jan. 2015. DOI:10.1016/j.asoc.2014.10.021
   Web of Science ®Google Scholar
• M. P. Eiselt, and A. B. Rosso, “Overview of the 1st international competition on plagiarism detection,” in 3rd PAN Workshop, Uncovering Plagiarism, Authorship and Social Software Misuse, 2009, pp. 1.
   Google Scholar
• A. Barrón-Cedeño, M. Potthast, P. Ross, B. Stein, and A. Eiselt, “Corpus and evaluation measures for automatic plagiarism detection”, Proceedings of the Seventh International Conference on Language Resources and Evaluation, Malta, May 2010, pp. 771–774.
   Google Scholar
• D. Bollegala, Y. Matsuo, and M. Ishizuka, “Measuring semantic similarity between words using web search engines,” www, Vol. 7, pp. 757–766, May 2007.
   Google Scholar
• S. A. Elavarasi, J. Akilandeswari, and K. Menaga, “A survey on semantic similarity measure,” Int J Res Advent Technol, Vol. 2, no. 3, pp. 389–398, Mar. 2014.
   Google Scholar
• H. T. Nguyen, P. H. Duong, and E. Cambria, “Learning short-text semantic similarity with word embeddings and external knowledge sources,” Knowl. Based. Syst., Vol. 182, pp. 104842, Oct. 2019. DOI:10.1016/j.knosys.2019.07.013
   Web of Science ®Google Scholar
• D. K. Tayal, A. Jain, A. Roy, and M. Gupta, “An Improved word similarity measure For ontological context,” in 2019 International Conference on Advances in Computing, Communication and Control (ICAC3), IEEE, Dec. 2019, pp. 1–5.
   Google Scholar
• Turnitin Plagiarism detection software. Available: https://www.turnitin.com/.
   Google Scholar
• Plagiarized text source. Available: https://en.wikipedia.org/wiki/Fonterra.
   Google Scholar