Natural Language Processing with Deep Learning: Recent Advances

Abstract

The discipline of natural language processing, or NLP, is quickly developing and has many uses, including question answering, sentiment analysis, and machine translation. Deep learning techniques have allowed models to learn complex patterns and representations directly from data, significantly advancing the state-of-the-art in NLP applications. This paper reviews the latest developments in deep learning techniques for "natural language processing (NLP)", highlighting notable innovations in neural network architectures, pretraining techniques, and fine-tuning processes. The results demonstrate that the model outperformed baseline techniques in terms of performance improvement. Ultimately, deep learning has expanded the capabilities of natural language processing (NLP) systems and made it possible to develop more accurate, flexible, and scalable language comprehension technologies.