Efficient methods for topic model inference on streaming document collections
@inproceedings{Yao2009EfficientMF, title={Efficient methods for topic model inference on streaming document collections}, author={Limin Yao and David Mimno and A. McCallum}, booktitle={KDD}, year={2009} }
Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of training documents requires approximate inference techniques that are computationally expensive. With today's large-scale, constantly expanding document collections, it is useful to be able to infer topic distributions for new documents without retraining the model. In this paper, we empirically evaluate the… CONTINUE READING
Supplemental Video
Topics from this paper
392 Citations
Efficient Methods for Inferring Large Sparse Topic Hierarchies
- Computer Science
- ACL
- 2015
- 8
- Highly Influenced
- PDF
Mr. LDA: a flexible large scale topic modeling package using variational inference in MapReduce
- Computer Science
- WWW
- 2012
- 124
- PDF
Linear Time Samplers for Supervised Topic Models using Compositional Proposals
- Computer Science
- KDD
- 2015
- 6
- PDF
Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections
- Computer Science
- EMNLP
- 2015
- 6
- PDF
References
SHOWING 1-3 OF 3 REFERENCES
Topic Models over Text Streams: A Study of Batch and Online Unsupervised Learning
- Computer Science
- SDM
- 2007
- 141
- Highly Influential
- PDF
Fast collapsed gibbs sampling for latent dirichlet allocation
- Mathematics, Computer Science
- KDD
- 2008
- 509
- Highly Influential
- PDF