• Prashant Wable
• Prasad Nikam
• Nikhil Gupta
• H.R. Kulkarni

This study explores the fundamental concepts, techniques, and applications of Natural Language Processing (NLP), a rapidly advancing field within artificial intelligence that enables computers to understand, interpret, and generate human language. With the increasing volume of digital text data, NLP has become essential for automating text analysis and enhancing human–computer interaction. The research examines key NLP processes such as text preprocessing, tokenization, stemming, lemmatization, part-of-speech tagging, and machine learning–based text classification. Practical experiments were conducted using standard NLP libraries and datasets to demonstrate how these techniques can be applied to extract meaningful information from unstructured text. The study also evaluates the performance of selected NLP models using common metrics to determine their accuracy and effectiveness. The findings highlight the potential of NLP in solving real-world problems across various domains, including sentiment analysis, document categorization, information retrieval, and conversational systems. Despite challenges such as language ambiguity and domain-specific variations, the results show that NLP techniques significantly enhance automated text processing and decision-making. This research contributes to a deeper understanding of NLP and provides a foundation for further exploration and advanced model development.

NATURAL LANGUAGE PROCESSING USING DEEP LEARNING

"NATURAL LANGUAGE PROCESSING USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.67-84, January-2026, Available :http://www.jetir.org/papers/JETIRHG06008.pdf

"NATURAL LANGUAGE PROCESSING USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. pp67-84, January-2026, Available at : http://www.jetir.org/papers/JETIRHG06008.pdf