Study on Extrinsic Text Plagiarism Detection Techniques and Tools

Vani, K.; Gupta, Deepa

doi:10.25103/jestr.094.23

Cited by 21 publications

(2 citation statements)

References 17 publications

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“…This proposed architecture algorithm is effective for intrinsic detection within the university database as well. Vani & Gupta (2016) claim that it's better for extrinsic plagiarism detection algorithms to classify queries to one of the following; string based, vector space model, semantic based, syntax based, structural based, or citation based. That classification is not presented in the reviewed paper, meaning all documents in the experiment were handled as a single class for extrinsic detection.…”

Section: Paper Methodologymentioning

confidence: 99%

Paper Review: Protecting Students’ Intellectual Property in the Web Plagiarism Detection Process

Almusawi¹

2019

IJoTE

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Students' from all over the world face an undisclosed infringement to their intellectual property. Mainly their written submissions in the form of essays, assignments and homework are archived in plagiarism detection services (PDS) without their consent. It is fair to say that these documents are never used for anything other than to report on similarity and cross matching newly submitted documents for plagiarism detection, or so it seems. However, it still is a form of infringement, especially when PDS providers are making profit out of the whole process. This paper reviews the potential application and implications of implementing the proposed architecture in the reviewed paper. The proposed architecture highlights a great resolution to the problem. This paper addresses an overview of the reviewed paper, as well as key notes to consider when adopting the proposed architecture.

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Section: Paper Methodologymentioning

confidence: 99%

Paper Review: Protecting Students’ Intellectual Property in the Web Plagiarism Detection Process

Almusawi¹

2019

IJoTE

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“…(3) Idea plagiarism detection tools are ineffective [10]. (4) Most PD methods may not detect structural alterations [11]. (5) Passage-level detections may lack linguistic, semantic, and soft computing tools.…”

Section: Introductionmentioning

confidence: 99%

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications

Darwish,

Mhaimeed,

Elzoghabi

2023

Entropy

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The majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism detection system in practice, and the system fails. In cases like active-to-passive conversion, phrase structure changes, synonym substitution, and sentence reordering, the present approaches may not be adequate for plagiarism detection. In this article, semantic extraction and the quantum genetic algorithm (QGA) are integrated in a unified framework to identify idea plagiarism with the aim of enhancing the performance of existing methods in terms of detection accuracy and computational time. Semantic similarity measures, which use the WordNet database to extract semantic information, are used to capture a document’s idea. In addition, the QGA is adapted to identify the interconnected, cohesive sentences that effectively convey the source document’s main idea. QGAs are formulated using the quantum computing paradigm based on qubits and the superposition of states. By using the qubit chromosome as a representation rather than the more traditional binary, numeric, or symbolic representations, the QGA is able to express a linear superposition of solutions with the aim of increasing gene diversity. Due to its fast convergence and strong global search capacity, the QGA is well suited for a parallel structure. The proposed model has been assessed using a PAN 13-14 dataset, and the result indicates the model’s ability to achieve significant detection improvement over some of the compared models. The recommended PD model achieves an approximately 20%, 15%, and 10% increase for TPR, PPV, and F-Score compared to GA and hierarchical GA (HGA)-based PD methods, respectively. Furthermore, the accuracy rate rises by approximately 10–15% for each increase in the number of samples in the dataset.

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Integrating syntax‐semantic‐based text analysis with structural and citation information for scientific plagiarism detection

Vani

Gupta

2018

Asso for Info Science & Tech

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The objective of the work is to explore the potency of integrating structural and citation information with effective syntax‐semantic text‐based analysis for scientific plagiarism detection. One of the major limitations in today's plagiarism checkers is their sole dependence on text‐based detection, where they ignore the citation and structural information. Further, the text‐based detection approaches that they employ usually fail to trace out intelligent manipulations. In the proposed work, a plagiarism detection system is presented that employs the effective coupling of various modules, namely, logical structure classifications and citation parsing, two‐stage candidate document selections, syntax‐semantic‐based exhaustive passage level analysis with plagiarism analysis using structural and citation information. Further, a new plagiarism score, namely, weighted overall similarity index is proposed, opposed to the general plagiarism scores. The proposed approach is evaluated on the data set created by Alzahrani et al. (), which contains scientific publications imposed with various plagiarism complexities. Comparison of the final system results is done against a potential baseline approach. The proposed approach exhibits considerable improvement over the comparative baseline, and hence reflects the potency of syntax‐semantic text‐based analysis with structural and citation information.

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Study on Extrinsic Text Plagiarism Detection Techniques and Tools

Cited by 21 publications

References 17 publications

Paper Review: Protecting Students’ Intellectual Property in the Web Plagiarism Detection Process

Paper Review: Protecting Students’ Intellectual Property in the Web Plagiarism Detection Process

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications

Integrating syntax‐semantic‐based text analysis with structural and citation information for scientific plagiarism detection

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