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NLP is a subfield of artificial intelligence (AI) and computer science that bridges the gap between human communication and machine understanding. It combines computational linguistics—the rule-based modeling of syntax and structure—with statistical modeling, machine learning, and deep learning. Ultimately, NLP allows computers to process, interpret, and generate text and speech just like a human would. [1, 2, 3]. Today, NLP forms the foundational architecture behind everything from Large Language Models (LLMs) like ChatGPT to the smart voice assistants you use daily.
The Two Core Pillars of NLP
To manage human communication, NLP systems split the workload into two primary engineering subfields: [1, 2]
Natural Language Understanding (NLU): Focuses on comprehension. It breaks down unstructured text to decipher intent, context, and meaning. Examples include detecting sentiment or recognizing names of places. [1, 2, 3]
Natural Language Generation (NLG): Focuses on creation. It translates structured, computer-readable data back into clear, conversational human text. Examples include writing summaries or generating responses to questions. [1, 2, 3, 4]