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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Multimedia Tools and Applications

ISSN

1380-7501

e-ISSN

—

Svazek periodika

2025

Číslo periodika v rámci svazku

2025

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

43475 - 43497

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105003842668