What is NLU?
NLU is the interpretive engine that allows machines to understand not just the words people say, but what they mean. It transforms raw text or speech into structured representations that software can reason about - recognizing intent (“book a flight”), entities (“New York,” “next week”), and sentiment (“frustrated,” “satisfied”).
Early NLU systems relied on symbolic logic and rules. Today, advanced Machine Learning (ML) and Large Language Models (LLMs) handle ambiguity and context dynamically - allowing AI assistants and chatbots to maintain natural, human-like conversations. NLU helps machines go beyond surface-level text - understanding context, intent, entities, and sentiment. It powers conversational AI systems, voice assistants, and intelligent search engines that can interpret questions and respond accurately.
How NLU Works
- Input Processing: Text or speech is tokenized and parsed into meaningful segments.
- Syntactic Analysis: Identifies grammatical structure and relationships.
- Semantic Analysis: Maps words and phrases to meaning using vector embeddings.
- Intent Recognition: Determines what the user wants to accomplish.
- Entity Extraction: Detects names, locations, or domain-specific terms.
- Sentiment Analysis: Measures tone, emotion, or polarity.
- Action Mapping: Links intent to an executable command or system response.
NLU works hand in hand with Natural Language Generation (NLG) to create full conversational loops - understanding input and producing relevant output.
