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A comprehensive overview of Natural Language Processing (NLP), beginning by defining it as a multidisciplinary field focused on enabling computers to understand, interpret, and generate human language. It details the historical progression of NLP through symbolic, statistical, and neural eras, culminating in the rise of Large Language Models (LLMs). The text then breaks down the mechanics of language understanding via the NLP pipeline, explaining processes like tokenization, syntactic analysis, and semantic analysis. Finally, it explores diverse applications of NLP across industries such as healthcare, finance, and legal tech, while also addressing significant challenges including ambiguity, computational cost, and ethical concerns like bias and privacy, before looking to the future of multimodal and explainable AI.