Breaking Ground in Plagiarism Detection: A Researcher’s Hybrid Lexical-Semantic Approach

In an era where the originality of academic and professional content is increasingly critical, a groundbreaking approach to plagiarism detection has emerged from a researcher at Stevens Institute of Technology. Shashankk Shekar Chaturvedi’s innovative work, combining lexical fingerprinting and transformer-based semantic embeddings, promises to revolutionize the fight against plagiarism.

Plagiarism Detection With Lexical-Semantic Approach

The Challenges of Detecting Plagiarism

Traditional plagiarism detection methods have long relied on lexical similarity measures, such as direct substring matching. However, these approaches falter when faced with semantically similar but lexically diverse paraphrasing. Recent advancements in artificial intelligence, particularly transformer models like BERT and Sentence-BERT, enable semantic similarity detection, but even these can miss trivial lexical overlaps. Chaturvedi’s hybrid model, which merges the best of both worlds, effectively addresses these limitations by integrating surface-level lexical patterns with deeper semantic relationships.

Innovative System Design

The proposed system employs a three-pronged methodology:

1. Lexical Fingerprinting: Using rolling hash and winnowing techniques, the system generates stable fingerprints that capture verbatim overlaps. These fingerprints form the foundation of the lexical similarity metric.

2. Semantic Embeddings: Transformer-based embeddings provide a nuanced understanding of the text, identifying conceptual similarities even when surface-level overlap is minimal.

3. Classification Models: The lexical and semantic similarity scores are combined using classifiers like Logistic Regression, Random Forest, and XGBoost, with the latter emerging as the best performer due to its stability and accuracy.

Experimental Success

The system was rigorously tested using the Quora Question Pairs dataset, which, while not specifically designed for plagiarism detection, effectively simulates paraphrasing scenarios. Results demonstrated a marked improvement over traditional methods:

• The hybrid approach achieved a 72.5% accuracy, outperforming both lexical-only (65%) and semantic-only (68%) models.
• The integration of XGBoost as the classifier further enhanced the robustness and reliability of the system.

Real-Time Application

A user-friendly Streamlit application was also developed to showcase the system’s capabilities. Users can input text pairs, adjust parameters, and view real-time similarity scores and predictions. This interactive feature highlights the practical usability of the hybrid model in diverse real-world scenarios, from academic institutions to content verification platforms.

Future Directions

While the current system sets a new standard in plagiarism detection, Chaturvedi envisions further refinements, including:

• Testing the model on dedicated plagiarism datasets for more comprehensive validation.
• Expanding to multilingual applications, a critical need in today’s globalized academic and professional landscapes.
• Incorporating larger and more advanced transformer models to enhance semantic analysis.

Conclusion

Chaturvedi’s work is not merely a technical achievement but a leap forward in ensuring academic integrity and professional accountability. By harmonizing the strengths of lexical and semantic analysis, this hybrid approach offers a robust and scalable solution to one of the most persistent challenges in content creation. The implications extend beyond plagiarism detection, opening avenues for applications in content moderation, knowledge graph construction, and more.

Stay tuned for a detailed exploration of this transformative research in the full article.

Reference

Chaturvedi, S. S. A Hybrid Lexical-Semantic Approach to Plagiarism Detection Leveraging Transformer Embeddings, Lexical Fingerprinting, and Algorithmic Comparison.

Declaration of Generative AI

This article generated using ChatGPT