Navigating the currents of natural language processing: A comprehensive overview of modern techniques and applications

Natural Language Processing (NLP) has been a trend over the years, and many young researchers are inspired to study this field. In this paper, the author conducts a comprehensive overview of NLP from rule-based systems to machine learning techniques. Besides, the core principles of NLP and the impact of deep learning architectures, such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformers, are explored. Additionally, this paper delves into significant NLP tasks, for instance, text classification, sentiment analysis, and machine translation. At the same time, the ethical considerations and challenges in NLP, including issues related to data bias and privacy, are also discussed. As a result, this research concludes that NLP is versatile and valuable, but it also needs to overcome challenges related to language ambiguity, data bias, privacy concerns, and so on. The ethical use of AI is necessary in order to ensure progress in NLP in the future.