Bayesian Parameter Estimation in LDA
Authors
Z.Y Liu, Y. Wang, W.P Wang, Z.Z Ji, W.Y Lu
Corresponding Author
Z.Y Liu
Available Online June 2015.
- DOI
- 10.2991/cisia-15.2015.225How to use a DOI?
- Keywords
- bayesian; parameter estimation; LDA; topic model
- Abstract
Latent Dirichlet Allocation (LDA) probabilistic topic model is widely used in text mining, natural language processing and so on. But LDA’s mathematical theory is particularly complex, thus it is very difficult to understand LDA for a novice. In order to more quickly and easily learn LDA, and further promote its application, this paper will deeply analyze LDA from the perspective of Bayesian parameter estimation. At first we explain the advantage of Bayesian parameter estimation by an instance, and then introduce a simple Bayesian Unigram model. Next based on the simple Bayesian Unigram model and PLSA model, a full Bayesian probabilistic topic model—LDA is presented.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Z.Y Liu AU - Y. Wang AU - W.P Wang AU - Z.Z Ji AU - W.Y Lu PY - 2015/06 DA - 2015/06 TI - Bayesian Parameter Estimation in LDA BT - Proceedings of the International Conference on Computer Information Systems and Industrial Applications PB - Atlantis Press SP - 837 EP - 840 SN - 2352-538X UR - https://doi.org/10.2991/cisia-15.2015.225 DO - 10.2991/cisia-15.2015.225 ID - Liu2015/06 ER -