Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on enabling machines to understand, interpret, and generate human language. The integration of machine learning (ML) techniques in NLP has led to remarkable advancements, transforming how computers interact with text and speech data. This paper provides a comprehensive review of machine learning methods applied to NLP, highlighting traditional approaches, deep learning architectures, and recent innovations. Key applications, including sentiment analysis, machine translation, chatbots, and text summarization, are discussed. Challenges such as data sparsity, ambiguity, and ethical considerations are addressed, along with recent trends such as pre-trained language models and transformers. The paper concludes with insights into future directions and potential research areas in ML-driven NLP.

Keywords: Natural Language Processing, Machine Learning, Deep Learning, Transformers, Sentiment Analysis, Language Models, Text Classification