Urdu paraphrased text reuse and plagiarism detection using pre-trained large language models and deep hybrid neural networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AD976RMJR" target="_blank" >RIV/00216208:11320/26:D976RMJR - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s11042-025-20862-7" target="_blank" >http://dx.doi.org/10.1007/s11042-025-20862-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11042-025-20862-7" target="_blank" >10.1007/s11042-025-20862-7</a>

Result language

angličtina

Original language name

Urdu paraphrased text reuse and plagiarism detection using pre-trained large language models and deep hybrid neural networks

Original language description

The growing prevalence of text reuse and plagiarism in various fields has led to an urgent need for reliable computational methods for detection. However, current commercial plagiarism detection systems are ineffective in identifying paraphrased cases of text reuse, highlighting the need for improvement. Previous research on paraphrased text reuse and plagiarism detection has mainly focused on English, European, Persian, and Arabic languages, and very few studies have been reported on the under-resourced Urdu language. This study aims to overcome this research gap by using a Deep Neural Network (DNN) based architecture and pre-trained Large Language Models (LLMs) for the task of Urdu paraphrased text reuse and plagiarism detection. The architecture called Deep Text Reuse and Paraphrased Plagiarism Detection (D-TRaPPD), relies on LLMs for input and utilizes CNN and LSTM to extract essential textual features. Moreover, we have proposed and evaluated two D-TRaPPD variants, Word Embeddings-D-TRaPPD (WE-D-TRaPPD) and Sentence Embeddings-D-TRaPPD (SE-D-TRaPPD), using two gold standard document-level corpora containing both real and simulated cases of Urdu paraphrased text reuse and plagiarism. The results demonstrate the effectiveness of the D-TRaPPD architecture, with SE-D-TRaPPD achieving the highest F1 scores of 91.77 for real cases and 95.15 for simulated cases. Furthermore, the results highlight the superiority of our approaches over the state-of-the-art methods for Urdu paraphrased text reuse and plagiarism detection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Multimedia Tools and Applications

ISSN

1380-7501

e-ISSN

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Volume of the periodical

2025

Issue of the periodical within the volume

2025

Country of publishing house

US - UNITED STATES

Number of pages

23

Pages from-to

43475 - 43497

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-105003842668