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Natural Language Processing, as applied to Gated Recurrent Units, involves the development of algorithms and models capable of understanding, interpreting, and generating human language in a valuable way. It focuses on the interaction between computers and human language, with the ultimate objective of creating systems that understand and respond to text or voice inputs similarly to how humans do. In this context, Natural Language Processing takes advantage of Gated Recurrent Units' ability to selectively capture long-term dependencies in data sequences. This makes GRUs particularly effective at handling tasks like language modeling, machine translation, sentiment analysis, and speech recognition, where the understanding of linguistic structures and context is crucial. By combining the power of deep learning with GRUs, Natural Language Processing can achieve impressive results in various applications, providing significant value to technology and artificial intelligence advancements.