Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling
Abstract
We develop dependent hierarchical normalized random measures and apply them to dynamic topic modeling. The dependency arises via superposition, subsampling and point transition on the underlying Poisson processes of these measures. The measures used include normalised generalised Gamma processes that demonstrate power law properties, unlike Dirichlet processes used previously in dynamic topic modeling. Inference for the model includes adapting a recently developed slice sampler to directly manipulate the underlying Poisson process. Experiments performed on news, blogs, academic and Twitter collections demonstrate the technique gives superior perplexity over a number of previous models.
Cite
Text
Chen et al. "Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling." International Conference on Machine Learning, 2012.Markdown
[Chen et al. "Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/chen2012icml-dependent/)BibTeX
@inproceedings{chen2012icml-dependent,
title = {{Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling}},
author = {Chen, Changyou and Ding, Nan and Buntine, Wray L.},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/chen2012icml-dependent/}
}