DEEP LEARNING IN NLP: HOW NEURAL NETWORKS ARE CHANGING LANGUAGE MODELS

This article analyzes the progression of natural language processing (NLP) via deep learning, emphasizing the transformative impact of neural network topologies on language model creation. It examines the transition from conventional rule-based and statistical methodologies to data-driven deep learning techniques adept in identifying intricate language patterns. The document presents a thematic summary of principal applications in natural language processing, encompassing text classification, machine translation, sentiment analysis, and syntactic parsing, while examining the influence of diverse deep learning methodologies, including recurrent neural networks, convolutional architectures, and transformer-based models. This study assesses methodology, datasets, pre-processing approaches, and assessment criteria from a wide range of literature, highlighting both the strengths and persistent shortcomings of existing models. Special emphasis is placed on difficulties in semantic comprehension, adaptation to low-resource environments, and computing requirements. The study continues by delineating prospective avenues for future research, encompassing model efficiency, flexibility, and enhanced language and cognitive integration within neural architectures.