ABSTRACT
There has been an explosion in the amount of digital text information available in recent years, leading to challenges of scale for traditional inference algorithms for topic models. Recent advances in stochastic variational inference algorithms for latent Dirichlet allocation (LDA) have made it feasible to learn topic models on very large-scale corpora, but these methods do not currently take full advantage of the collapsed representation of the model. We propose a stochastic algorithm for collapsed variational Bayesian inference for LDA, which is simpler and more efficient than the state of the art method. In experiments on large-scale text corpora, the algorithm was found to converge faster and often to a better solution than previous methods. Human-subject experiments also demonstrated that the method can learn coherent topics in seconds on small corpora, facilitating the use of topic models in interactive document analysis software.
- A. Asuncion, M. Welling, P. Smyth, and Y. Teh. On smoothing and inference for topic models. In Uncertainty in Artificial Intelligence, 2009. Google ScholarDigital Library
- D. C. Atkins, T. N. Rubin, M. Steyvers, M. A. Doeden, B. R. Baucom, and A. Christensen. Topic models: A novel method for modeling couple and family text data. Journal of Family Psychology, 6:816--827, 2012.Google ScholarCross Ref
- A. Banerjee and S. Basu. Topic models over text streams: A study of batch and online unsupervised learning. In SIAM Data Mining, 2007.Google Scholar
- J. Bezanson, S. Karpinski, V. B. Shah, and A. Edelman. Julia: A fast dynamic language for technical computing. CoRR, abs/1209.5145, 2012.Google Scholar
- D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. The Journal of Machine Learning Research, 3:993--1022, 2003. Google ScholarDigital Library
- J. Boyd-Graber, J. Chang, S. Gerrish, C. Wang, and D. Blei. Reading tea leaves: How humans interpret topic models. In Proceedings of the 23rd Annual Conference on Neural Information Processing Systems, 2009.Google Scholar
- O. Cappé and E. Moulines. On-line expectation--maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(3):593--613, 2009.Google Scholar
- B. Carpenter. Integrating out multinomial parameters in latent Dirichlet allocation and naive bayes for collapsed Gibbs sampling. Technical report, LingPipe, 2010.Google Scholar
- T. Griffiths and M. Steyvers. Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1):5228, 2004.Google ScholarCross Ref
- M. Hoffman, D. Blei, C. Wang, and J. Paisley. Stochastic variational inference. arXiv preprint arXiv:1206.7051, 2012.Google Scholar
- M. D. Hoffman, D. M. Blei, and F. Bach. Online learning for latent Dirichlet allocation. Advances in Neural Information Processing Systems, 23:856--864, 2010.Google ScholarDigital Library
- D. Mimno. Computational historiography: Data mining in a century of classics journals. Journal on Computing and Cultural Heritage (JOCCH), 5(1):3, 2012. Google ScholarDigital Library
- D. Mimno. Reconstructing pompeian households. Uncertainty in Artificial Intelligence, 2012.Google Scholar
- D. Mimno, M. Hoffman, and D. Blei. Sparse stochastic inference for latent Dirichlet allocation. In Proceedings of the International Conference on Machine Learning, 2012.Google Scholar
- T. Minka. Power EP. Technical report, Microsoft Research, Cambridge, UK, 2004.Google Scholar
- D. Newman, A. Asuncion, P. Smyth, and M. Welling. Distributed algorithms for topic models. The Journal of Machine Learning Research, 10:1801--1828, 2009. Google ScholarDigital Library
- I. Porteous, D. Newman, A. Ihler, A. Asuncion, P. Smyth, and M. Welling. Fast collapsed gibbs sampling for latent dirichlet allocation. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 569--577, 2008. Google ScholarDigital Library
- I. Sato and H. Nakagawa. Rethinking collapsed variational Bayes inference for LDA. Proceedings of the International Conference on Machine Learning, 2012.Google Scholar
- A. Smola and S. Narayanamurthy. An architecture for parallel topic models. Proceedings of the VLDB Endowment, 3(1--2):703--710, 2010. Google ScholarDigital Library
- Y. Teh, D. Newman, and M. Welling. A collapsed variational bayesian inference algorithm for latent Dirichlet allocation. Advances in Neural Information Processing Systems, 19:1353, 2007.Google Scholar
- L. Yao, D. Mimno, and A. McCallum. Efficient methods for topic model inference on streaming document collections. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 937--946. ACM, 2009. Google ScholarDigital Library
- Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation
Recommendations
Stochastic variational inference
We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet ...
A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation
NIPS'06: Proceedings of the 19th International Conference on Neural Information Processing SystemsLatent Dirichlet allocation (LDA) is a Bayesian network that has recently gained much popularity in applications ranging from document modeling to computer vision. Due to the large scale nature of these applications, current inference procedures like ...
Practical collapsed variational bayes inference for hierarchical dirichlet process
KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data miningWe propose a novel collapsed variational Bayes (CVB) inference for the hierarchical Dirichlet process (HDP). While the existing CVB inference for the HDP variant of latent Dirichlet allocation (LDA) is more complicated and harder to implement than that ...
Comments