CONTEXT-AWARE PLAGIRASIM DETECTION FOR PHARAPHRASED CONTENT

Plagiarism detection is an important task in academic and professional fields due to the increasing availability
of digital content. Traditional systems mainly detect exact text matching but fail to identify paraphrased
content where the meaning is retained but wording is changed.
This project proposes a Context-Aware Plagiarism Detection System that uses Natural Language Processing
(NLP) techniques to analyze the semantic similarity between documents. The system performs preprocessing
steps such as tokenization, stopword removal, and stemming, followed by feature extraction using TF-IDF.
Similarity between texts is calculated using cosine similarity to determine the level of plagiarism.
The system provides a similarity percentage along with highlighted matched content, making it easier for users
to understand the results. The proposed approach improves accuracy in detecting paraphrased plagiarism
compared to traditional methods. This system can be effectively used in academic institutions and content
verification platforms to ensure originality and maintain integrity