Performance Optimization Of N-Gram Generation in Natural Language Processing Systems Through Parallel Computing

This study investigates optimization approaches for an N-gram generation module employed in natural language processing systems. The research focuses on enhancing the module's performance through the implementation of parallel processing techniques. Experimental evaluation demonstrated a substantial reduction in N-gram generation time, improved utilization of processor resources, and preservation of output accuracy. The findings indicate that the proposed optimization methods are effective for processing large-scale text corpora and can significantly improve the efficiency of N-gram-based language processing tasks.

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