Topic Model

Statistical algorithms, also known as probabilistic topic models, designed to unveil the latent semantic structures within extensive text bodies. These models assist in organizing and providing insights into large collections of unstructured text, aiding comprehension in the era of information overload. Originally developed for text mining, topic models have expanded their utility to detect instructive structures in various data types, including genetic information, images, and networks, with applications in fields like bioinformatics. Jaxon utilizes topic models to generate labels, leveraging their ability to discern meaningful themes and patterns within data.