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Paraphrase detection for Urdu language text using fine-tune BiLSTM framework.

Muhammad Ali Aslam1, Khairullah Khan1, Wahab Khan1

  • 1Department of Computer Science, University of Science and Technology, Bannu, 28100, Pakistan.

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|May 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bidirectional Long Short-Term Memory (BiLSTM) framework for automated paraphrase detection in Urdu, achieving 94.14% accuracy on a custom corpus. The research also presents a large-scale Urdu Paraphrased Corpus (UPC) to advance NLP research.

Keywords:
BiLSTMCNNLSTMNLPParaphrase detectionUrdu text

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Area of Science:

  • Natural Language Processing (NLP)
  • Computational Linguistics
  • Artificial Intelligence (AI)

Background:

  • Automated paraphrase detection is vital for NLP tasks like summarization and plagiarism detection.
  • Urdu paraphrase detection faces challenges due to complex morphology, script, and limited resources.
  • Existing methods struggle with Urdu's linguistic nuances.

Purpose of the Study:

  • To develop a robust framework for Urdu paraphrase detection.
  • To address the complexities of Urdu language in paraphrase identification.
  • To create a valuable resource for Urdu NLP research.

Main Methods:

  • Proposed a novel Bidirectional Long Short-Term Memory (BiLSTM) framework.
  • Utilized word embeddings and text preprocessing (tokenization, stop-word removal, label encoding).
  • Developed a large-scale Urdu Paraphrased Corpus (UPC) with 150,000 manually verified paraphrase pairs.

Main Results:

  • The BiLSTM model achieved 94.14% accuracy on the custom UPC dataset.
  • Outperformed Convolutional Neural Network (CNN) at 83.43% and Long Short-Term Memory (LSTM) at 88.09%.
  • Attained 95.34% accuracy on the benchmark Quora dataset, demonstrating broad applicability.

Conclusions:

  • The proposed BiLSTM framework significantly improves Urdu paraphrase detection performance.
  • The created Urdu Paraphrased Corpus (UPC) serves as a crucial resource for future research.
  • The model's robustness is enhanced by a linguistic rule engine for exceptional cases.