Dialogue

Natural Language Understanding in Chatbots Natural Language Understanding (NLU) is the part of a chatbot that makes sense of what people say. It goes beyond recognizing words; it tries to grasp intent and the important details that guide the next step in a conversation. When a user asks for help or makes a request, strong NLU turns casual speech into structured data that a bot can act on. Two core tasks are intent detection and entity extraction. Intent detection answers: what does the user want to do? Entity extraction finds details like dates, places, or product names. Example: User says “I need a flight to Paris next Friday.” The system identifies intent book_flight and entities destination=Paris, date=next Friday. This structured result lets the bot plan a response rather than guess what the user means. ...

Natural Language Interfaces: Voice and Text as UX Natural language interfaces turn spoken words and written messages into a usable experience. Instead of clicking through menus, people describe what they want and the system acts, suggests, or asks for a clarification. This approach fits quick tasks and personal interactions, like checking the weather, setting a timer, or asking for help in a chat. Voice and text are both powerful ways to communicate with technology. ...

Natural Language Understanding in Chatbots Natural Language Understanding (NLU) is the part of a chatbot that makes sense of what a user says. It turns words into a plan the bot can act on. Good NLU handles variation in language, tone, and mistakes, so conversations feel natural rather than robotic. What NLU Does NLU splits input into two main pieces: intent and entities. The intent answers “what does the user want?” while entities extract concrete details like dates, places, or quantities. Together they guide the next step in the dialogue. ...

Natural Language Understanding in Chatbots Natural Language Understanding (NLU) is the part of a chatbot that makes sense of user text. It looks for what the user wants and what details matter. Good NLU helps a bot respond correctly, even when wording changes. It is a key part of conversational AI. How NLU fits into a chatbot workflow Text is preprocessed: case, typos, and odd spacing are normalized. Predictions: the system guesses the user intent and finds key details (slots). Clarifications: if the meaning is unclear, the bot may ask a short question. Action and reply: the bot uses intent and data to call services and craft a reply. Learning: feedback from real chats improves the model over time. What NLU handles well ...