The Relative Review of Machine Learning in Natural Language Processing (NLP)

Abstract

Natural Language Processing (NLP) has experienced essential transformations thanks to machine learning (ML) technical implementations. This research discusses the integrated nature of NLP and ML through data driven methodologies that led to radical evolution in text classification and sentiment analysis and machine translation and question-answering systems. Deep learning architectures, particularly transformers like BERT, GPT, and T5, which utilize extensive datasets and contextual embeddings, have largely replaced traditional rule-based methods. This study examines crucial ML approaches, such as supervised, unsupervised, and reinforcement learning, and assesses their influence on NLP performance metrics. Additionally, the paper explores current trends, including prompt engineering, fine-tuning of large language models, and ethical issues in AI-powered NLP applications. By consolidating recent developments, this investigation aims to offer perspectives on the future direction of ML in NLP and its potential impact across various industries and real-world applications. The applications are Email filters, Smart assistants, Search results, Predictive text, Language translation, Digital phone calls, Data analysis, Text analytics.