NATURAL LANGUAGE PROCESSING: RECENT DEVELOPMENTS, TRENDS, AND DIFFICULTIES

Abstract

Natural Language Processing (NLP) is a rapidly advancing domain within Artificial Intelligence (AI) that allows computers to comprehend, manipulate, and produce human language. This document provides a summary of the latest advancements, burgeoning trends, and significant obstacles in natural language processing. It examines the progression of NLP from rule-based frameworks to transformer architectures and Large Language Models (LLMs), as well as the uses of Natural Language Understanding (NLU) and Natural Language Generation (NLG). The document moreover examines prominent NLP development frameworks, including as Transformer, BERT, and GPT models, and underscores their contemporary uses in diverse fields. Furthermore, it explores significant obstacles like linguistic ambiguity, multilingual processing, computational intricacies, and ethical dilemmas, while delineating prospective study avenues for the advancement of more effective and dependable NLP systems.