The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Topics over time: a non-Markov continuous-time model of topical trends
Abstract
This paper presents an LDA-style topic model that captures not only the low-dimensional structure of data, but also how the structure changes over time. Unlike other recent work that relies on Markov assumptions or discretization of time, here each topic is associated with a continuous distribution over timestamps, and for each generated document, the mixture distribution over topics is influenced by both word co-occurrences and the document's timestamp. Thus, the meaning of a particular topic can be relied upon as constant, but the topics' occurrence and correlations change significantly over time. We present results on nine months of personal email, 17 years of NIPS research papers and over 200 years of presidential state-of-the-union addresses, showing improved topics, better timestamp prediction, and interpretable trends.
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Cites
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Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
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Blei, D. M., & Lafferty, J. D. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143859 · Cited by 2,386
Hierarchical Dirichlet Processes
Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000302 · Cited by 2,120
The author-topic model for authors and documents
Rosen‐Zvi, M., Griffiths, T. L., Steyvers, M., & Smyth, P. (2004) · Uncertainty in Artificial Intelligence · doi:10.5555/1036843.1036902 · Cited by 1,280
Bursty and hierarchical structure in streams
Kleinberg, J. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775061 · Cited by 1,160
A generalized probability density function for double-bounded random processes
Kumaraswamy, P. (1980) · Journal of Hydrology · doi:10.1016/0022-1694(80)90036-0 · Cited by 1,023
Griffiths, T. L., Mark Steyvers, B., Blei, D. M., & Tenenbaum, J. B. (2004) · Cited by 501
Mixed-membership models of scientific publications
Erosheva, E., Fienberg, S., & Lafferty, J. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307760101 · Cited by 442
Cited by
Group formation in large social networks: membership, growth, and evolution
Backstrom, L., Huttenlocher, D., Kleinberg, J., & Lan, X. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150412 · Cited by 1,805
How to analyze political attention with minimal assumptions and costs
Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R. (2010) · American Journal of Political Science · doi:10.1111/j.1540-5907.2009.00427.x · Cited by 671
Studying the history of ideas using topic models
Hall, D., Jurafsky, D., & Manning, C. D. (2008) · Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08 · doi:10.3115/1613715.1613763 · Cited by 191
Topic based classification and pattern identification in patents
Venugopalan, S., & Rai, V. (2015) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2014.10.006 · Cited by 116
Ranking related news predictions
Kanhabua, N., Blanco, R., & Matthews, M. (2011) · Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval · doi:10.1145/2009916.2010018 · Cited by 24
Metadata
- Authors
- Wang, X., & McCallum, A.
- Venue
- Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2006-08-20
- Pages
- 424-433
- OpenAlex
- W2171343266
- Cited by
- 1,121
- References
- 18
- FWCI
- 21.85
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wang, X., & McCallum, A. (2006). Topics over time: a non-Markov continuous-time model of topical trends. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/1150402.1150450
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
