Plagiarism Detection Using Artificial Intelligence
Abstract
Abstract—Presently available plagiarism detection technologies are primarily restricted to string-level comparisons between potentially original texts and suspiciously plagiarized materials. The objective of this research is to enhance the precision of plagiarism identification by integrating Natural Language Processing (NLP) methods into current methodologies. Our proposal is an external plagiarism detection framework that uses various natural language processing (NLP) approaches to examine a set of original and suspicious papers. The techniques not only analyze text strings but also the text's structure, taking text relations into consideration. Preliminary findings using a corpus of short paragraphs that have been plagiarized demonstrate that NLP approach increase the correctness of current methods.
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A. Aiken, “MOSS: A System for Detecting Software Plagiarism,” Stanford University, 1994. [Accessed: Mar. 21, 2010]. Available: https://stanford.edu
T. Lancaster and F. Culwin, "Classifications of plagiarism detection engines," unpublished internal 2nd draft, South Bank University, London, UK, 2003, pp. 1-16.
J. Pennington, R. Socher, and C. D. Manning, “Glove: Global vectors for word representation,” in Proc. 2014 Conf. Empirical Methods Natural Lang. Process. (EMNLP), Oct. 2014, pp. 1532-1543.
J. Ramos, “Using tf-idf to determine word relevance in document queries,” in Proc. 1st Instructional Conf. Machine Learning, vol. 242, Dec. 2003, pp. 133-142.
V. Thada and V. Jaglan, “Comparison of jaccard, dice, cosine similarity coefficient to find best fitness value for web retrieved documents using genetic algorithm,” Int. J.Innovations Eng. Technol., vol. 2, no. 4, pp. 202-205, Aug. 2013.
D. Klein and C. D. Manning, “Fast Exact Inference with a Factored Model for Natural Language Parsing,” in Advances Neural Inf. Process. Syst. 15 (NIPS 2002), Cambridge, MA: MIT Press, 2003, pp. 3-10.
N. Awale, M. Pandey, A. Dulal, and B. Timsina, “Plagiarism Detection in Programming Assignments using Machine Learning,” Journal of Artificial Intelligence and Capsule Networks, vol. 2, no. 3, pp. 177-184, Jul. 2020. [Online]. Available: http://irojournals.com/aicn/. DOI: https://doi.org/10.36548/jaicn.2020.3.005
H. Chavan, M. Taufik, R. Kadave, and N. Chandra, “Plagiarism Detector Using Machine Learning,” International Journal of Research in Engineering, Science and Management, vol. 4, no. 4, pp. [specific pages], Apr. 2021. [Online]. Available: https://www.ijresm.com. ISSN (Online): 2581-5792.
R. Rosu, A. S. Stoica, P. S. Popescu, and M. C. Mihăescu, “NLP based Deep Learning Approach for Plagiarism Detection,” International Journal of User-System Interaction, vol. 13, no. 1, pp. 48-60, 2020.
M. Chong, L. Specia, and R. Mitkov, “Using Natural Language Processing for Automatic Detection of Plagiarism,” presented at the [Conference/Workshop], Research Group in Computational Linguistics, University of Wolverhampton, UK.
J. Bao and J. Malcolm, “Text similarity in academic conference papers,” in 2nd International Plagiarism Conference, Northumbria University Press, 2006, pp. 19-21.
J. Bao, C. Lyon, P. Lane, W. Ji, and J. Malcolm, “Comparing Different Methods to Detect Text Similarity,” Technical Report, University of Hertfordshire, 2007.
A. Barrón-Cedeño and P. Rosso, "Towards the Exploitation of Statistical Language Models for Plagiarism Detection with Reference," in European Conference on Artificial Life, ECAL 2008 PAN Workshop, 2008, pp. 15-19.
N. Kang, A. Gelbukh, and S. Han, "Ppchecker: Plagiarism pattern checker in document copy detection," in Text, Speech and Dialogue, Springer, 2006, pp. 661-667.
T. Lancaster and F. Culwin, “A Visual Argument for Plagiarism Detection using Word Pairs,” in Plagiarism: Prevention, Practice and Policies 2004 Conference, Apr. 2004, pp. 1-14..
R. Lukashenko, V. Graudina, and J. Grundspenkis, “Computer-Based Plagiarism Detection Methods and Tools: An Overview,” in ACM International Conference on Computer Systems and Technologies, vol. 54, no. 3, pp. 203-215, 2007.
J. Williams, “The plagiarism problem: Are students entirely to blame?” in Proc. 19th Annual Conf. Australasian Society for Computers in Learning in Tertiary Education, ASCILITE, 2009, pp. 934-937
DOI: https://doi.org/10.29040/ijcis.v5i2.170
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