The Evolution of Machine Learning in Natural Language Processing: From Traditional Methods to Deep Learning

Abstract

Natural language processing (NLP) has undergone a significant evolution from traditional methods to deep learning. Early rule-based parsing and statistical machine learning methods, such as hidden Markov models and conditional random fields, relied on manual feature engineering, which, although effective in tasks like text classification, had limited generalization capabilities. With the rise of deep learning, technologies represented by Recurrent Neural Network (RNNs), Transformers, and pre-trained models (Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-Trained Transformer (GPT)) have achieved breakthroughs in semantic understanding and generation through end-to-end learning, significantly enhancing the performance of machine translation and question-answering systems. The limitations of traditional methods in feature design, long text modeling, and computational efficiency have gradually become apparent, while deep learning has demonstrated its advantages through automatic feature extraction and high-dimensional data processing. Research has also explored the potential of integrating traditional and deep learning approaches (such as Bi-directional Long Short-Term Memory - conditional random field (BiLSTM-CRF)) to enhance interpretability, highlighting the need to focus on model lightweighting, multimodal integration, and adaptation to low-resource languages in the future. This article systematically organizes the developments in NLP technology, providing researchers with a panoramic perspective while emphasizing the necessity of standardized datasets and validation methods.