Advancements in Natural Language Processing: A Survey of Recent Research
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Abstract
Deep learning, neural network architectures, and large-scale language models have all brought about substantial developments in Natural Language Processing (NLP) in recent years. These gains have been driven by the achievements in these areas. In this survey study, an overview of the most recent research trends and breakthroughs in natural language processing (NLP) is presented, with a focus on the most important successes, problems, and future prospects. the fast development of natural language processing (NLP) and its widespread applications across a variety of fields, including as machine translation, sentiment analysis, question answering, and information retrieval. It highlights the revolutionary influence of deep learning approaches, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer models, in pushing the frontiers of natural language processing (NLP) performance. the most important topics that were discussed in the survey, which included new developments in pre-trained language models like BERT, GPT, and XLNet, as well as their applicability in downstream natural language processing chores. In addition to this, it examines developing subjects such as multilingual natural language processing, zero-shot learning, and few-shot learning, all of which have received a large amount of interest from scholars working in the field.
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