Conditional Random Field

Conditional Random Fields (CRFs) are probabilistic graphical models used for structured prediction, aiming to model the dependencies between variables to improve prediction accuracy in sequence labeling tasks. Current research focuses on enhancing CRF performance through various architectures, including neural CRFs (often combined with deep learning models like transformers), semi-Markov CRFs for handling variable-length segments, and high-order CRFs to capture complex interactions between variables. These advancements are significantly impacting diverse fields, improving performance in applications such as named entity recognition, image segmentation, and human trajectory prediction, often surpassing traditional methods and even showing competitive results against large language models in specific tasks.