Evolution and advancements in natural language processing: from representation learning to deep neural networks

Natural Language Processing (NLP) has undergone a remarkable transformation, with representation learning playing a pivotal role in reshaping the field. This review explores the evolution of NLP representation learning, from traditional feature engineering to modern techniques, focusing on distributed representations and deep learning methods. We delve into the significance of word embeddings like Word2Vec and GloVe, which have become instrumental in enhancing text processing. Furthermore, we examine common deep learning architectures, including Multilayer Perceptrons (MLP) and Convolutional Neural Networks (CNNs), highlighting their role in feature extraction from textual data. Additionally, optimization methods crucial for efficient deep neural network training are discussed. This review provides a comprehensive overview of the advancements in NLP architecture, emphasizing the enduring impact of prior research and the promise of future innovations.