TL;DR
Experimental results indicate that the proposed approach achieves competitive performance compared to existing plagiarism detection systems, and the comparative analysis highlights the strengths and limitations of different word embedding models across datasets.
The findings underscore the potential of advanced NLP techniques to overcome language-specific challenges, providing a foundation for future research in multilingual plagiarism detection and enhancing the development of tools for other languages facing similar challenges.
It is determined that the proposed hybrid model attains higher correlation with human judgment than standalone or traditional baselines, and is an efficient and scalable solution that balances computational performance with semantic accuracy for practical tasks.
The results suggest that integrating diverse similarity measures with neural networks enhances the identification of both explicit and nuanced paraphrases, thereby supporting advancements in text analysis and plagiarism detection systems.
This study introduces an external plagiarism detection framework built on an artificial neural network model and a lexical feature extraction framework adapted to the linguistic features of Arabic, verifying its effectiveness for Arabic plagiarism detection.
Preliminary evidence is provided that the two-level scheme is feasible as an initial semantic-similarity indicator for plagiarism screening tool for Indonesian-language student assignment documents, with the final judgment of plagiarism remaining with the examiner.
This study validates the effectiveness of the BERT model in semantic similarity calculation, providing more accurate technical support for related application scenarios, and laying the foundation for subsequent model optimization and lightweighting research.
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