Pachinko allocation

Technopedia Center

Faculty of Engineering and Computer Science
S1 Informatics S1 Information Systems S1 Information Technology S1 Computer Engineering S1 Electrical Engineering S1 Civil Engineering

faculty of Economics and Business
S1 Management S1 Accountancy

Faculty of Letters and Educational Sciences
S1 English literature S1 English language education S1 Mathematics education S1 Sports Education

Pachinko allocation - Wikipedia

From Wikipedia, the free encyclopedia

This article relies excessively on references to primary sources. Please improve this article by adding secondary or tertiary sources.
Find sources: "Pachinko allocation" – news · newspapers · books · scholar · JSTOR (September 2010) (Learn how and when to remove this message)

In machine learning and natural language processing, the pachinko allocation model (PAM) is a topic model. Topic models are a suite of algorithms to uncover the hidden thematic structure of a collection of documents. ^[1] The algorithm improves upon earlier topic models such as latent Dirichlet allocation (LDA) by modeling correlations between topics in addition to the word correlations which constitute topics. PAM provides more flexibility and greater expressive power than latent Dirichlet allocation.^[2] While first described and implemented in the context of natural language processing, the algorithm may have applications in other fields such as bioinformatics. The model is named for pachinko machines—a game popular in Japan, in which metal balls bounce down around a complex collection of pins until they land in various bins at the bottom.^[3]

History

Pachinko allocation was first described by Wei Li and Andrew McCallum in 2006.^[3] The idea was extended with hierarchical Pachinko allocation by Li, McCallum, and David Mimno in 2007.^[4] In 2007, McCallum and his colleagues proposed a nonparametric Bayesian prior for PAM based on a variant of the hierarchical Dirichlet process (HDP).^[2] The algorithm has been implemented in the MALLET software package published by McCallum's group at the University of Massachusetts Amherst.

Model

This section needs expansion. You can help by adding to it. (July 2017)

PAM connects words in V and topics in T with an arbitrary directed acyclic graph (DAG), where topic nodes occupy the interior levels and the leaves are words.

The probability of generating a whole corpus is the product of the probabilities for every document:^[3]

$P(\mathbf {D} |\alpha )=\prod _{d}P(d|\alpha )$

References

^ Blei, David. "Topic modeling". Archived from the original on 2 October 2012. Retrieved 4 October 2012.
^ ^a ^b Li, Wei; Blei, David; McCallum, Andrew (2007). "Nonparametric Bayes Pachinko Allocation". arXiv:1206.5270. {{cite journal}}: Cite journal requires |journal= (help)
^ ^a ^b ^c Li, Wei; McCallum, Andrew (2006). "Pachinko allocation: DAG-structured mixture models of topic correlations" (PDF). Proceedings of the 23rd international conference on Machine learning - ICML '06. pp. 577–584. doi:10.1145/1143844.1143917. ISBN 1595933832. S2CID 13160178.
^ Mimno, David; Li, Wei; McCallum, Andrew (2007). "Mixtures of hierarchical topics with Pachinko allocation" (PDF). Proceedings of the 24th international conference on Machine learning. pp. 633–640. doi:10.1145/1273496.1273576. ISBN 9781595937933. S2CID 6045658.
^ Hofmann, Thomas (1999). "Probabilistic Latent Semantic Indexing" (PDF). Proceedings of the Twenty-Second Annual International SIGIR Conference on Research and Development in Information Retrieval. Archived from the original (PDF) on 14 December 2010.
^ 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. Archived from the original on 1 May 2012. Retrieved 19 July 2010.

External links

Mixtures of Hierarchical Topics with Pachinko Allocation, a video recording of David Mimno presenting HPAM in 2007.

Natural language processing

General terms

Text analysis

Text segmentation	Compound-term processing Lemmatisation Lexical analysis Text chunking Stemming Sentence segmentation Word segmentation

Automatic summarization

Machine translation

Distributional semantics models

Language resources,
datasets and corpora

Types and standards	Corpus linguistics Lexical resource Linguistic Linked Open Data Machine-readable dictionary Parallel text PropBank Semantic network Simple Knowledge Organization System Speech corpus Text corpus Thesaurus (information retrieval) Treebank Universal Dependencies
Data	BabelNet Bank of English DBpedia FrameNet Google Ngram Viewer UBY WordNet Wikidata