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

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

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...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Vol. 25; no. 9; p. 1271
Main Authors: Darwish, Saad M., Mhaimeed, Ibrahim Abdullah, Elzoghabi, Adel A.
Format: Journal Article
Language:English
Published: Basel MDPI AG 29-08-2023
MDPI
Subjects:
Accuracy
Algorithms
Computational linguistics
Computing time
Datasets
Detectors
Documents
Genetic algorithms
Information storage
Language processing
Learning strategies
Linguistics
Machine learning
Natural language interfaces
optimization
Optimization techniques
Plagiarism
plagiarism detection
Quantum computing
quantum evolutionary algorithms
Qubits (quantum computing)
Representations
semantic analysis
Semantics
Sentences
Similarity
United Kingdom
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Summary: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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ISSN:1099-4300
1099-4300
DOI:10.3390/e25091271