How does Natural Language Understanding NLU work

Natural language understanding (NLU) works by processing human language through multiple computational stages to extract meaning, intent, and structured information from unstructured text or speech. The process begins with tokenization that breaks text into words or subwords, followed by part-of-speech tagging and syntactic parsing to identify grammatical relationships. Modern NLU systems employ transformer-based neural networks that use self-attention mechanisms to capture contextual dependencies and semantic relationships across entire sequences. Core components include named entity recognition for identifying specific entities, intent classification for determining user goals, and slot filling for extracting relevant parameters. The system leverages pre-trained language models that encode semantic knowledge, enabling understanding of context, ambiguity resolution, and inference. Advanced NLU incorporates dialogue state tracking, coreference resolution, and pragmatic understanding for conversational contexts. For AI agents, NLU enables natural communication interfaces, instruction interpretation, and contextual reasoning essential for autonomous task completion.