Title: Lexical and Hierarchical Topic Regression
Authors: Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik

Inspired by a two-level theory from political science that unifies agenda setting and ideological framing, we propose supervised hierarchical latent Dirichlet allocation (SHLDA), which jointly captures documents’ multi-level topic structure and their polar response variables. Our model extends the nested Chinese restaurant processes to discover tree-structured topic hierarchies and uses both per-topic hierarchical and per-word lexical regression parameters to model response variables. SHLDA improves prediction on political affiliation and sentiment tasks in addition to providing insight into how topics under discussion are framed.

Title: Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent
Authors: Yuening Hu, Jordan Boyd-Graber, Hal Daume III, Z. Irene Ying

Discovering hierarchical regularities in data is a key problem in interacting with large datasets, modeling cognition, and encoding knowledge. A previous Bayesian solution---Kingman's coalescent---provides a probabilistic model for data represented as a binary tree. Unfortunately, this is inappropriate for data better described by bushier trees. We generalize an existing belief propagation framework of Kingman's coalescent to the beta coalescent, which models a wider range of tree structures.  Because of the complex combinatorial search over possible structures, we develop new sampling schemes using sequential Monte Carlo and Dirichlet process mixture models, which render inference efficient and tractable.  We present results on synthetic and real data that show the beta coalescent outperforms Kingman's coalescent and is qualitatively better at capturing data in bushy hierarchies.