Implementation Patterns of Natural Language Processing Using Pre-Trained Deep Learning Models
Keywords:
Deep Learning, Pre-Trained Deep Learning Models, Natural Language ProcessingAbstract
When it comes to computer programming, Natural Language Processing (NLP) is all about creating computers that can read and respond to information in the same way that humans do, and then generate their text or speech in response. "Natural language processing" is an area of artificial intelligence (AI) that aims to give computers the ability to understand written and spoken language in the same way humans do (NLP). Using a combination of computational linguistics and statistics, machine learning, and deep learning models, NLP uses a set of programmable rules to describe human language. When text and voice data are combined, computers may 'understand' human language, including the speaker's or writer's intent and emotion, in the form of text or audio data. To put it another way, NLP is the driving force behind computer systems that translate text across languages, respond to spoken commands, and summaries massive volumes of information quickly—even in real-time. When it comes to voice-activated GPS, digital assistants, speech-to-text software, and customer support chatbots, you've utilized NLP as a consumer. Improved operational efficiency, increased employee productivity, and simplified mission-critical business operations are all benefits of using NLP in corporate solutions. Assorted deep learning systems for NLP analytics and research aspects are presented in the manuscript.
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