Author(s): Markus Fischer

Abstract: Natural Language Processing (NLP) plays a crucial role in enhancing user interaction with chatbots, making them more intuitive and effective. As conversational agents, chatbots are increasingly utilized across various industries, from customer service to healthcare, education, and e-commerce. The core functionality of chatbots is rooted in their ability to process and understand natural language, allowing them to interpret user queries, provide relevant responses, and even predict user intent. NLP technologies, including tokenization, named entity recognition, part-of-speech tagging, and sentiment analysis, enable chatbots to bridge the communication gap between human language and machine understanding. Despite the advancements, challenges remain in achieving seamless interactions due to issues such as language ambiguity, context understanding, and the ability to process complex queries. This paper aims to explore the evolving role of NLP in chatbots, examine the technological advancements that have enhanced chatbot performance, and address the existing challenges and limitations. The research further investigates the integration of deep learning models, such as transformers, in enhancing NLP's capabilities for chatbot systems. Additionally, the paper explores the potential of NLP to personalize user experiences and adapt to diverse communication styles. This review also discusses future trends in chatbot development, including multilingual capabilities, emotional intelligence, and context-aware responses, which aim to make these systems more human-like. The paper concludes with a reflection on the impact of NLP advancements on the future of human-computer interaction, highlighting the potential for chatbots to become indispensable tools in enhancing customer engagement and satisfaction.

DOI: 10.33545/27076571.2026.v7.i1a.241

Pages: 35-38 | Views: 53 | Downloads: 23

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