The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Pachinko allocation: DAG-structured mixture models of topic correlations
Abstract
Latent Dirichlet allocation (LDA) and other related topic models are increasingly popular tools for summarization and manifold discovery in discrete data. However, LDA does not capture correlations between topics. In this paper, we introduce the pachinko allocation model (PAM), which captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). The leaves of the DAG represent individual words in the vocabulary, while each interior node represents a correlation among its children, which may be words or other interior nodes (topics). PAM provides a flexible alternative to recent work by Blei and Lafferty (2006), which captures correlations only between pairs of topics. Using text data from newsgroups, historic NIPS proceedings and other research paper corpora, we show improved performance of PAM in document classification, likelihood of held-out data, the ability to support finer-grained topics, and topical keyword coherence.
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Cites
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Cited by
Blei, D. M. (2012) · Communications of the ACM · doi:10.1145/2133806.2133826 · Cited by 5,516
topicmodels : An R Package for Fitting Topic Models
Grün, B., & Hornik, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v040.i13 · Cited by 1,072
Adaptive Federated Optimization
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., & McMahan, H. B. (2020) · arXiv · doi:10.48550/arxiv.2003.00295 · Cited by 840
Gaining insights from social media language: Methodologies and challenges.
Kern, M. L., Park, G., Eichstaedt, J. C., Schwartz, H. A., Sap, M., Smith, L. K., & Ungar, L. H. (2016) · Psychological Methods · doi:10.1037/met0000091 · Cited by 221
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
Metadata
- Authors
- Li, W., & McCallum, A.
- Venue
- Proceedings of the 23rd international conference on Machine learning - ICML '06
- Publisher
- ACM Press
- Published
- 2006
- Pages
- 577-584
- OpenAlex
- W2106490775
- Cited by
- 609
- References
- 1
- FWCI
- 18.73
- Type
- conference paper
- Language
- en
- Open access
- closed
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