Bridging Language Structure and Deep Learning Models: A Comprehensive Exploration of Natural Language Processing

Abstract

Natural language processing (NLP) is currently seen as a pillar of our digital age. Due to its expanding importance and widespread use, there is a need for thorough comprehension and ongoing research. This study delves further into NLP’s complexities than it does on the surface. By exploring if common themes, like discussing the weather or analyzing peas, can provide a universal grammar rule that can produce all English sentences, we hope to grasp the basic structure of language. By doing this, we hope to ease the challenging process of teaching computers to comprehend and produce human language. This study addresses the dynamic world of deep learning (DL) models in NLP in addition to language structure. It provides a thorough evaluation of the numerous models and approaches used in various NLP applications. Understanding the advantages and disadvantages of various strategies is crucial for helping academics and practitioners select the models that are best suited for particular NLP tasks. This work also acts as a knowledge bridge by bridging the gap between DL and NLP. It describes how DL approaches have transformed NLP, 104providing improved functionality for jobs like text analysis, translation, and chatbots. We clarify the profound influence of DL on the development of NLP, opening it up to a larger audience. This work greatly contributes to the ongoing discussion on the development of language processing technology by offering insights into language structure and undertaking a comparative examination of DL models in NLP. For researchers, programmers, and companies wishing to use NLP and DL in their applications, it provides useful information.