This repository has Java implementation of some interesting topic models based on Latent Dirichlet Allocation (LDA) [1].
Currently it includes the implementation of Entity-Topic models [2][3], the Author-Topic model [4] and Special Words with Background (SWB) model [5]. For each of the topic models, the model parameters are estimated using Gibbs Sampling.
The code shared in this repository is inspired from the simple single file implementation of Gibbs Sampler for LDA shared here.
You can compile the source with the compile-all.sh shell script.
As a demonstration the Ci-Lda model is trained on the data provided in data/trial-et, using a command 'like':
java -cp bin/ CiLdaGibbsSampler data/trial-et/non-entity-term-index data/trial-et/non-entity-vocab data/trial-et/entity-term-index data/trial-et/entity-vocab 25 0.01 0.01 0.01
For more topic models and their implementations check here.
References:
[1] David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. J. Mach. Learn. Res. 3 (March 2003), 993-1022.
[2] David Newman, Chaitanya Chemudugunta, Padhraic Smyth. Statistical entity-topic models. KDD (2006).
[3] Linmei Hu, Juanzi Li, Zhihui Li, Chao Shao, Zhixing Li. Incorporating Entities in News Topic Modeling. NLPCC 2013, CCIS 400.
[4] Michal Rosen-Zvi, Thomas Griffiths, Mark Steyvers, and Padhraic Smyth. 2004. The author-topic model for authors and documents. UAI '04.
[5] Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers. Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model. NIPS 2006: 241-248.