A Hybrid NLP-Driven Paraphrasing System Leveraging NLTK and Machine Learning
Suresh Babu P, Deanne Charan, Yelichalamala Shakeer, Shaik Azra, Valluru Husna · 2025
Good paraphrasing is essential in the digital age for automated text generation, information retention, and unique content. To improve sentence structure, fluency, and semantic coherence, our NLP-Based Paraphrasing System combines rule-based methods (NLTK, WordNet) with deep learning (BERT, GPT). Tokenization, synonym replacement, dependency parsing, and semantic consistency checks are some of the clever ways the system processes text to provide high-quality paraphrased output. It is perfect for academic, content-creation, and AI-driven applications since it uses an adaptive learning paradigm that allows it to improve results based on input. This method establishes a new benchmark for text transformation by guaranteeing precision, readability, and context preservation.