Abstract
In this paper we propose a Dirichlet process topic model for representing patterns in complex dynamic data. We assume that in a dynamic corpus topics do not randomly appear and disappear but rather replace each other following transitional rules. We model topic transitions as a Markov chain with an infinite hidden Markov model. The proposed model is then formulated as a Dirichlet process with a hierarchical Dirichlet process hidden Markov model prior.
| Original language | English |
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| Publication status | Published - 2018 |
| Event | All of Bayesian Nonparametrics (Especially the Useful Bits): A workshop at the Thirty-Second Annual Conference on Neural Information Processing Systems (NeurIPS 2018). - Palais des Congrès de Montréal, Montréal, Canada Duration: 7 Dec 2018 → 7 Dec 2018 https://sites.google.com/view/nipsbnp2018/home |
Workshop
| Workshop | All of Bayesian Nonparametrics (Especially the Useful Bits) |
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| Abbreviated title | BNP@NeurIPS 2018 |
| Country/Territory | Canada |
| City | Montréal |
| Period | 7/12/18 → 7/12/18 |
| Internet address |