Exploring Neural Networks for Natural Language Processing Applications

Abstract

The integration of Neural Networks (NN) into Natural Language Processing (NLP) has revolutionized numerous language-based applications, offering enhanced performance in tasks ranging from text classification to machine translation. Neural networks, especially deep learning models such as Recurrent Neural Networks (RNNs) and Transformer models, have demonstrated the ability to model complex language structures and dependencies effectively. This article explores the evolution of neural network architectures, focusing on their application in NLP. It covers key advancements, practical applications, challenges faced, and the future of neural network models in improving NLP systems. The research highlights the capabilities of neural networks to handle vast linguistic datasets, addressing problems such as contextual understanding, ambiguity resolution, and scalability in real-world applications.