Natural Language Text

Natural language text processing focuses on enabling computers to understand, generate, and manipulate human language, aiming to bridge the gap between human communication and machine intelligence. Current research heavily utilizes large language models (LLMs) and transformer architectures, focusing on tasks like information extraction, data-to-text generation, and cross-lingual understanding, often incorporating techniques like prompting strategies and graph-based methods to improve accuracy and interpretability. These advancements have significant implications for various fields, including improving search engines, automating knowledge graph construction, and enhancing the efficiency of clinical data analysis.