Multi-Field Hierarchical Discovery and Tracking
Modeling information dynamics at different levels of granularity is an open challenge. We are developing new Bayesian VonMieses-Fischer topical clustering techniques, including hierarchical and dynamic models that outperform existing methods and scale to large data. Our approach consists of multi-field graphical models for correlated latent topics, semi-supervised topology learning, metric learning, transfer learning and temporal trend modeling. We evaluate on large datasets of scientific literature, as well as news story collections.