Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream

Amr Ahmed, Eric Xing
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:20-29, 2010.

Abstract

Topic models have proven to be a useful tool for discovering latent structures in document collections. However, most document collec- tions often come as temporal streams and thus several aspects of the latent structure such as the number of topics, the topics’ dis- tribution and popularity are time-evolving. Several models exist that model the evolu- tion of some but not all of the above as- pects. In this paper we introduce infinite dynamic topic models, iDTM, that can ac- commodate the evolution of all the aforemen- tioned aspects. Our model assumes that doc- uments are organized into epochs, where the documents within each epoch are exchange- able but the order between the documents is maintained across epochs. iDTM allows for unbounded number of topics: topics can die or be born at any epoch, and the repre- sentation of each topic can evolve according to a Markovian dynamics. We use iDTM to analyze the birth and evolution of topics in the NIPS community and evaluated the effi- cacy of our model on both simulated and real datasets with favorable outcome.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-ahmed10a, title = {Timeline: A Dynamic Hierarchical {D}irichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream}, author = {Ahmed, Amr and Xing, Eric}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {20--29}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/ahmed10a/ahmed10a.pdf}, url = {https://proceedings.mlr.press/r8/ahmed10a.html}, abstract = {Topic models have proven to be a useful tool for discovering latent structures in document collections. However, most document collec- tions often come as temporal streams and thus several aspects of the latent structure such as the number of topics, the topics’ dis- tribution and popularity are time-evolving. Several models exist that model the evolu- tion of some but not all of the above as- pects. In this paper we introduce infinite dynamic topic models, iDTM, that can ac- commodate the evolution of all the aforemen- tioned aspects. Our model assumes that doc- uments are organized into epochs, where the documents within each epoch are exchange- able but the order between the documents is maintained across epochs. iDTM allows for unbounded number of topics: topics can die or be born at any epoch, and the repre- sentation of each topic can evolve according to a Markovian dynamics. We use iDTM to analyze the birth and evolution of topics in the NIPS community and evaluated the effi- cacy of our model on both simulated and real datasets with favorable outcome.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream %A Amr Ahmed %A Eric Xing %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-ahmed10a %I PMLR %P 20--29 %U https://proceedings.mlr.press/r8/ahmed10a.html %V R8 %X Topic models have proven to be a useful tool for discovering latent structures in document collections. However, most document collec- tions often come as temporal streams and thus several aspects of the latent structure such as the number of topics, the topics’ dis- tribution and popularity are time-evolving. Several models exist that model the evolu- tion of some but not all of the above as- pects. In this paper we introduce infinite dynamic topic models, iDTM, that can ac- commodate the evolution of all the aforemen- tioned aspects. Our model assumes that doc- uments are organized into epochs, where the documents within each epoch are exchange- able but the order between the documents is maintained across epochs. iDTM allows for unbounded number of topics: topics can die or be born at any epoch, and the repre- sentation of each topic can evolve according to a Markovian dynamics. We use iDTM to analyze the birth and evolution of topics in the NIPS community and evaluated the effi- cacy of our model on both simulated and real datasets with favorable outcome. %Z Reissued by PMLR on 04 October 2026.
APA
Ahmed, A. & Xing, E.. (2010). Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:20-29 Available from https://proceedings.mlr.press/r8/ahmed10a.html. Reissued by PMLR on 04 October 2026.

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