Natural Language Understanding (NLU)
What is natural language understanding (NLU)?
Natural language understanding (NLU) is a branch of AI that uses computer software to understand input in sentences using speech or text. NLU enables computer-human interaction, and it is the comprehension of human languages that allows computers to understand commands without the formalized syntax of computer languages. NLU also enables computers to communicate back to humans in their languages.
What are the applications of NLU?
- IVR and message routing: Interactive Voice Response (IVR) is used for self-service and call routing. NLU has broadened its capabilities, and users can interact with the phone system via voice.
- Customer support and service through intelligent personal assistants: NLU is the technology behind chatbots, a computer program that converses with a human in natural language via voice or text. Chatbots follow a script and can only answer questions in that script. They are used to provide answers to frequently asked questions, which involves layers of different processes in NLU technology.
- Machine translation: In the case of machine translation, an ML algorithm analyzes millions of pages of text to learn how to translate them into another language. If a user translates data with an automatic language tool such as a dictionary, it will substitute word-for-word. However, using machine translation will look up the words in context, which helps return a more accurate translation.
- Data capture: This is the process of gathering and recording information about an event, object, or person. If an e-commerce company used NLU, it could ask customers to verbally enter their billing and shipping information. The software would understand what the customer meant and enter the information automatically.
- Conversational interfaces: Many voice-activated devices allow users to speak naturally. Using NLU, conversational interfaces can understand and respond to human language by segmenting sentences and words, recognizing grammar, and using semantic knowledge to infer intent.
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