The Impact of Natural Language Processing on Streamlined Operations in U.S. Manufacturing and Logistics: Enhancing Productivity
Keywords:
Natural Language Processing, Manufacturing, LogisticsAbstract
Natural Language Processing (NLP) has emerged as a pivotal technology with diverse applications across various fields, including machine translation, email spam detection, information extraction, summarization, and medical question answering [1]. NLP, a branch of Artificial Intelligence and Linguistics, focuses on enabling computers to comprehend human languages, which are essentially sets of rules or symbols used for conveying information. NLP encompasses a range of tasks such as Automatic Summarization, Co-Reference Resolution, Discourse Analysis, Machine Translation, Named Entity Recognition, Optical Character Recognition, and Part Of Speech Tagging.
Recent advancements in computational power and the availability of extensive linguistic data have driven the need for automating semantic analysis using data-driven approaches, particularly through the utilization of deep learning methods in NLP [2]. These advancements have significantly improved core NLP tasks and applications, further enhancing the understanding and processing of human language for linguistic-based human-computer communication. This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning, demonstrating the pervasive influence of data-driven strategies in advancing NLP.
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