Extracting common narratives from multi-author dynamic text corpora requires complex models, such as the Dynamic Author Persona (DAP) topic model. However, such models are complex and can struggle to scale to large corpora, often because of challenging non-conjugate terms. To overcome such challenges, we adapt new ideas in approximate inference to the DAP model, resulting in the DAP Performed Exceedingly Rapidly (DAPPER) topic model. Specifically, we develop Conjugate-Computation Variational Inference (CVI) based variational Expectation-Maximization (EM) for learning the model, yielding fast, closed form updates for each document, replacing iterative optimization in earlier work. Our results show significant improvements in model fit and training time without needing to compromise the model's temporal structure or the application of Regularized Variation Inference (RVI). We demonstrate the scalability and effectiveness of the DAPPER model on multiple datasets, including the CaringBridge corpus - a collection of 9 million journals written by 200,000 authors during health crises.
|Original language||English (US)|
|Title of host publication||2018 IEEE International Conference on Data Mining, ICDM 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Dec 27 2018|
|Event||18th IEEE International Conference on Data Mining, ICDM 2018 - Singapore, Singapore|
Duration: Nov 17 2018 → Nov 20 2018
|Name||Proceedings - IEEE International Conference on Data Mining, ICDM|
|Conference||18th IEEE International Conference on Data Mining, ICDM 2018|
|Period||11/17/18 → 11/20/18|
Bibliographical noteFunding Information:
The research was supported by NSF grants IIS-1563950, IIS-1447566, IIS- 1447574, IIS-1422557, CCF-1451986, CNS-1314560
ACKNOWLEDGMENTS We thank reviewers for their valuable comments, University of Minnesota Supercomputing Institute for technical support, and CaringBridge for support and collaboration. The research was supported by NSF grants IIS-1563950, IIS-1447566, IIS-1447574, IIS-1422557, CCF-1451986, CNS-1314560.
© 2018 IEEE.
- Approximate inference
- Graphical model
- Non-conjugate models
- Regularized variational inference
- Text mining
- Topic modeling