Natural Language Processing Models: A Comparative Perspective

Abstract

Natural Language Processing is a thriving branch of artificial intelligence with diverse applications across multiple domains. In recent years, advances in machine learning models for NLP tasks have resulted in a parallel development in NLP methodologies. These models are capable of performing complicated NLP tasks such language translation, sentiment analysis, text categorization, and text production. This study reviews the NLP models by analyzing the traditional models, such as rule-based systems and statistical models, and then move on to the recent neural network and deep learning models. Natural Language Processing (NLP) is a branch of artificial intelligence with diverse applications across multiple domains. In recent years, advances in machine learning models for NLP tasks have resulted in a parallel development of NLP methodologies. These models are capable of performing complicated NLP tasks such as language translation, sentiment analysis, text categorization, and text production.