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Modeling word burstiness using the Dirichlet distribution
Abstract
Multinomial distributions are often used to model text documents. However, they do not capture well the phenomenon that words in a document tend to appear in bursts: if a word appears once, it is more likely to appear again. In this paper, we propose the Dirichlet compound multinomial model (DCM) as an alternative to the multinomial. The DCM model has one additional degree of freedom, which allows it to capture burstiness. We show experimentally that the DCM is substantially better than the multinomial at modeling text data, measured by perplexity. We also show using three standard document collections that the DCM leads to better classification than the multinomial model. DCM performance is comparable to that obtained with multiple heuristic changes to the multinomial model. 1.
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A vector space model for automatic indexing
Salton, G., Wong, A., & Yang, C. S. (1975) · Communications of the ACM · doi:10.1145/361219.361220 · Cited by 7,521
Cited by
Topics over time: a non-Markov continuous-time model of topical trends
Wang, X., & McCallum, A. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150450 · Cited by 1,121
A dirichlet multinomial mixture model-based approach for short text clustering
Yin, J., & Wang, J. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623715 · Cited by 561
Metadata
- Authors
- Madsen, R. E., Kauchak, D., & Elkan, C.
- Venue
- Proceedings of the 22nd international conference on Machine learning - ICML '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 545-552
- OpenAlex
- W2135631383
- Cited by
- 280
- References
- 1
- FWCI
- 8.11
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Modeling word burstiness using the Dirichlet distribution. https://app.cochat.ai/papers/10.1145/1102351.1102420
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- Madsen, R. E., Kauchak, D., & Elkan, C. (2005). Modeling word burstiness using the Dirichlet distribution. Proceedings of the 22nd international conference on Machine learning - ICML '05. https://doi.org/10.1145/1102351.1102420
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