INTELLIGENT PARAPHRASE RECOGNITION USING ADVANCED NLP TECHNIQUES

Abstract

The rapid growth of textual data across digital platforms has heightened the need for intelligent systems capable of understanding semantic similarity between sentences. This study presents an Intelligent Paraphrase Recognition System that leverages advanced Natural Language Processing (NLP) techniques to accurately identify whether two sentences convey the same meaning despite differences in structure or vocabulary. The proposed model integrates transformer-based architectures such as BERT and RoBERTa with semantic similarity measures and contextual embeddings to capture deep linguistic and contextual relationships between text pairs. Unlike traditional lexical-based approaches, this system emphasizes contextual understanding, enabling it to recognize paraphrases even in the presence of idiomatic expressions, rephrasing, or syntactic variations. The model undergoes fine-tuning on large-scale benchmark datasets such as Quora Question Pairs and Microsoft Research Paraphrase Corpus (MRPC) to ensure high generalization and reliability. Experimental results demonstrate that the proposed approach achieves superior accuracy, precision, and recall compared to conventional methods, establishing it as a robust and scalable solution for applications in plagiarism detection, question answering, text summarization, and semantic search. Index Terms – Paraphrase recognition, Natural Language Processing (NLP), BERT, RoBERTa, transformer models, semantic similarity, contextual embeddings, sentence pair classification, text similarity, deep learning, semantic search, plagiarism detection, question answering.