Idea plagiarism detection with recurrent neural networks and vector space model
International Journal of Intelligent Computing and Cybernetics
ISSN: 1756-378X
Article publication date: 26 March 2021
Issue publication date: 15 July 2021
Abstract
Purpose
Natural languages have a fundamental quality of suppleness that makes it possible to present a single idea in plenty of different ways. This feature is often exploited in the academic world, leading to the theft of work referred to as plagiarism. Many approaches have been put forward to detect such cases based on various text features and grammatical structures of languages. However, there is a huge scope of improvement for detecting intelligent plagiarism.
Design/methodology/approach
To realize this, the paper introduces a hybrid model to detect intelligent plagiarism by breaking the entire process into three stages: (1) clustering, (2) vector formulation in each cluster based on semantic roles, normalization and similarity index calculation and (3) Summary generation using encoder-decoder. An effective weighing scheme has been introduced to select terms used to build vectors based on K-means, which is calculated on the synonym set for the said term. If the value calculated in the last stage lies above a predefined threshold, only then the next semantic argument is analyzed. When the similarity score for two documents is beyond the threshold, a short summary for plagiarized documents is created.
Findings
Experimental results show that this method is able to detect connotation and concealment used in idea plagiarism besides detecting literal plagiarism.
Originality/value
The proposed model can help academics stay updated by providing summaries of relevant articles. It would eliminate the practice of plagiarism infesting the academic community at an unprecedented pace. The model will also accelerate the process of reviewing academic documents, aiding in the speedy publishing of research articles.
Keywords
Acknowledgements
This work is supported by Technical Education Quality Program - TEQIP III. The project is implemented by NPIU, which is a unit of MHRD, Govt of India for implementation of world bank assisted projects in Technical Education.
Citation
Nazir, A., Mir, R.N. and Qureshi, S. (2021), "Idea plagiarism detection with recurrent neural networks and vector space model", International Journal of Intelligent Computing and Cybernetics, Vol. 14 No. 3, pp. 321-332. https://doi.org/10.1108/IJICC-11-2020-0178
Publisher
:Emerald Publishing Limited
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