Travel services increasingly depend on digital interfaces through which visitors search for destinations, transport, accommodation, attractions, local information, and assistance. Natural Language Processing (NLP) provides a direct mechanism for converting human language into searchable, actionable information and can therefore become a key intelligence layer for smart tourism systems. This paper proposes a conceptual framework for multilingual intelligent travel assistants that combines natural-language query understanding, semantic search, conversational interaction, sentiment analysis, recommendation, and context-aware response generation. The framework is motivated by earlier work on natural-language searching of social networking sites, semantic-web based retrieval, query classification, and social-network traceability, and extends these ideas to travel information environments. The paper also considers Hindi, Urdu, and code-switched language settings, drawing on research by Shagufta Fatema on multilingual chatbots, generative AI for Hindi and Urdu language teaching, and sentiment analysis of Hindi and Urdu e-learning feedback. The proposed framework identifies a layered architecture from language input to intent detection, entity extraction, semantic retrieval, personalization, response generation, and feedback learning. The study is conceptual and does not claim experimental performance results. It concludes that multilingual NLP can improve accessibility and reduce the friction of travel information discovery, while privacy, hallucination, bias, multilingual resource scarcity, research and evaluation remain important challenges.
Natural language processing; smart tourism; multilingual chatbot; semantic search; travel assistant; sentiment analysis; conversational AI; Hindi; Urdu; personalization.