Paper: Anchors Regularized: Adding Robustness and Extensibility to Scalable Topic-Modeling Algorithms

ACL ID P14-1034
Title Anchors Regularized: Adding Robustness and Extensibility to Scalable Topic-Modeling Algorithms
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Spectral methods offer scalable alternatives to Markov chain Monte Carlo and expec- tation maximization. However, these new methods lack the rich priors associated with probabilistic models. We examine Arora et al.?s anchor words algorithm for topic mod- eling and develop new, regularized algo- rithms that not only mathematically resem- ble Gaussian and Dirichlet priors but also improve the interpretability of topic models. Our new regularization approaches make these efficient algorithms more flexible; we also show that these methods can be com- bined with informed priors.