Jeffrey Lund, Piper Armstrong, Wilson Fearn, Stephen Cowley, Courtni Byun, Jordan Boyd-Graber, Kevin Seppi. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.

We propose Labeled Anchors, an interactive and supervised topic model based on the anchor words algorithm (Arora et al., 2013).Labeled Anchors is similar to Supervised Anchors (Nguyen et al., 2014) in that it extends the vector-space representation of words to include document labels.However, our formulation also admits a classifier which requires no training beyond inferring topics, which means our approach is also fast enough to be interactive.We run a small user study that demonstrates that untrained users can interactively update topics in order to improve classification accuracy.