Natural Language Processing (NLP) and Natural Language Understanding (NLU) are interrelated fields within AI, but they focus on different aspects of language interaction. NLP is a broad domain that involves processing, analyzing, and generating human language in text or speech form. Tasks like tokenization, text generation, and translation fall under NLP’s umbrella. For instance, converting speech to text or summarizing a document is part of NLP.
NLU, a subset of NLP, focuses on interpreting the meaning and intent behind text or speech. It involves understanding the semantics, context, and relationships in language, making it more specific than general NLP. For example, in a chatbot, NLP might process the user’s query, while NLU determines its intent—such as identifying that "What’s the weather today?" seeks weather information. NLU also handles complex tasks like sentiment analysis, entity extraction, and intent recognition.
The key distinction lies in focus: NLP processes language broadly, while NLU emphasizes comprehension and context. Both are essential for applications like virtual assistants, where NLP handles text processing and NLU ensures accurate interpretation. Together, they enable machines to interact intelligently with human language.