Natural Language Understanding (NLU)

Natural Language Understanding, abbreviated NLU, is the part of language technology concerned with extracting meaning from text or speech. Typical tasks include intent classification, entity recognition, semantic parsing, question answering, relationship extraction, and determining how context changes interpretation. Modern NLU systems rely heavily on pretrained language models, though rules, knowledge graphs, and domain vocabularies are still useful where precision and control matter. Understanding remains imperfect because language is ambiguous, culturally situated, and dependent on shared knowledge. Strong benchmark scores may not transfer to new dialects, specialized terminology, sarcasm, or adversarial phrasing, so deployment requires representative testing and mechanisms for uncertainty or escalation.