Current Applications of Deep Learning Models in Natural Language Processing Technology
Keywords:
Natural Language Processing, Deep learning, GTP-3, Google, Language processingAbstract
This article traces the evolution of natural language processing (NLP) technologies from early statistical models like N-grams to contemporary deep learning frameworks such as recurrent neural networks (RNNs) and transformer-based architectures like BERT and GPT. It discusses how these advancements have revolutionized tasks like information extraction and machine translation, enabling more efficient linguistic data processing and reducing the dependency on manually labelled datasets. Despite challenges such as computational intensity, modern NLP continues to push boundaries in understanding and generating human language, driving significant advancements in various practical applications.
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