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 M. Mimno and Andrew 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
Highly Influential
This paper has highly influenced 45 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 387 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 253 extracted citations

Efficient Topic Modeling on Phrases via Sparsity

2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) • 2017
View 7 Excerpts
Highly Influenced

MetaLDA: A Topic Model that Efficiently Incorporates Meta Information

2017 IEEE International Conference on Data Mining (ICDM) • 2017
View 5 Excerpts
Highly Influenced

Sub-Gibbs Sampling: A New Strategy for Inferring LDA

2017 IEEE International Conference on Data Mining (ICDM) • 2017
View 7 Excerpts
Highly Influenced

Fast Online EM for Big Topic Modeling

IEEE Transactions on Knowledge and Data Engineering • 2016
View 20 Excerpts
Highly Influenced

Advances in Services Computing

Lina Yao, Xia Xie, +3 authors Hai Jin
Lecture Notes in Computer Science • 2015
View 11 Excerpts
Highly Influenced

388 Citations

050'10'12'14'16'18
Citations per Year
Semantic Scholar estimates that this publication has 388 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.

Similar Papers

Loading similar papers…