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Outline

DOC-SHEILD Plagiarism Detector

Abstract

In the world of academia and profession, original thought and authenticity form the bedrock. With the rise of plagiarism detection, intellectual property is now protected. Traditional plagiarism detectors face the challenge of detecting paraphrased, translated, or contextually altered content. This paper would describe a proposed system in which NLP, deep learning techniques, and advanced linguistic analysis would be applied in order to enhance the accuracy and efficiency of plagiarism detection. The proposed system would then integrate context-aware algorithms along with semantic similarity assessment over the limitations that the traditional methods have to their advantages, which might potentially raise the educational integrity of institutions and the authenticity of published works.

References (4)

  1. References
  2. Dong, Y., et al. "A Semantic-Based Plagiarism Detection Approach Using Word Embeddings." *Journal of Educational Computing Research*, vol. 57, no. 1, 2019.
  3. Soni, S., and Roberts, R. "Deep Learning for Paraphrase Detection." *Proceedings of the 12th ACM Conference on Text Mining*, 2021.
  4. Leacock, C., Chodorow, M., and Gamon, M. "Contextual Similarity in Plagiarism Detection: Improving Accuracy through Deep Learning." *Natural Language Engineering*, vol. 28, no. 2, 2022.