DEVELOPMENT OF AN INTELLIGENT CHATBOT SYSTEM USING NATURAL LANGUAGE PROCESSING (NLP)

Abstract

With the rapid growth of digital communication and online services, chatbots have emerged as one of the most widely adopted intelligent interfaces between humans and computer systems. Traditional rule-based chatbots are often rigid and unable to handle natural, diverse user queries. This paper presents the development of an intelligent chatbot system using Natural Language Processing (NLP) techniques, capable of understanding user intent, generating context-aware responses, and improving over time. The proposed system integrates modules for text preprocessing, intent classification, named entity recognition, dialogue management, and response generation. Machine learning and deep learning models are employed to classify user intents and map them to appropriate responses. A prototype chatbot is implemented and evaluated on metrics such as response accuracy, user satisfaction, and error rate. The results indicate that NLP-driven chatbots provide higher flexibility and better conversational experience compared to rule-based systems. The paper concludes with a discussion on applications, limitations, and future enhancements including multilingual support, emotion analysis, and integration with external knowledge bases.