Pachinko allocation
In machine learning and natural language processing, the pachinko allocation model (PAM) is a topic model, i.e. a generative statistical model for discovering the abstract "topics" that occur in a collection of documents. The algorithm improves upon earlier topic models such as latent Dirichlet allocation by modeling correlations between topics in addition to the word correlations which constitute topics. While first described and implemented in the context of natural language processing, the algorithm may have applications in other fields such as bioinformatics. The name comes from pachinko, a type of Japanese gaming machine related to pinball.
History
Pachinko allocation was first described by Wei Li and Andrew McCallum in 2006.[1] The idea was extended with hierarchical Pachinko allocation by Li, McCallum, and David Mimno in 2007.[2] The algorithm has been implemented in the MALLET software package published by McCallum's group at the University of Massachusetts, Amherst.
See also
References
- ^ Li, Wei; McCallum, Andrew (2006). "Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations". Proceedings of the 23rd International Conference on Machine Learning. http://www.cs.umass.edu/~mccallum/papers/pam-icml06.pdf.
- ^ Mimno, David; Li, Wei; McCallum, Andrew (2007). "Mixtures of Hierarchical Topics with Pachinko Allocation". Proceedings of the 24th International Conference on Machine Learning. http://www.machinelearning.org/proceedings/icml2007/papers/453.pdf.
- ^ Hofmann, Thomas (1999). "Probabilistic Latent Semantic Indexing". Proceedings of the Twenty-Second Annual International SIGIR Conference on Research and Development in Information Retrieval. http://www.cs.brown.edu/~th/papers/Hofmann-SIGIR99.pdf.
- ^ Blei, David M.; Ng, Andrew Y.; Jordan, Michael I; Lafferty, John (January 2003). "Latent Dirichlet allocation". Journal of Machine Learning Research 3: pp. 993–1022. doi:10.1162/jmlr.2003.3.4-5.993. http://jmlr.csail.mit.edu/papers/v3/blei03a.html.
External links