Natural Language Processing Models and Applications

Abstract

This paper reviews the technological evolution of Natural Language Processing (NLP), tracing its development from traditional rule-based and statistical methods to modern deep learning paradigms. It particularly emphasizes the profound impact of neural models. By systematically examining NLP’s significantly enhanced capabilities in understanding, generating, and integrating natural language, this study aims to comprehensively analyze the practical value of this technology across diverse application domains. The paper first elaborates on the limitations of rule-based and statistical models, then demonstrates the emerging capabilities of large-scale language models in cross-modal data fusion through applications such as medical text mining and data generation. Finally, it highlights current challenges faced by NLP, including robustness, computational resource consumption, and ethical biases, while proposing that lightweight models and multimodal unified frameworks represent critical future research directions. This review not only underscores the transformative potential of NLP across various fields but also provides insights into future research trajectories, encouraging the development of more efficient, interpretable, and ethically responsible language technologies.