2009 IncorporatingDomainKnowledgeInt

• (Andrzejewski et al., 2009) ⇒ David Andrzejewski, Xiaojin Zhu, and Mark Craven. (2009). “Incorporating Domain Knowledge Into Topic Modeling via Dirichlet Forest Priors.” In: Proceedings of the 26th Annual International Conference on Machine Learning. doi:10.1145/1553374.1553378

Subject Headings: Constrained Latent Dirichlet Allocation Algorithm

Cited By

• http://scholar.google.com/scholar?q=%22Incorporating+domain+knowledge+into+topic+modeling+via+Dirichlet+Forest+priors%22+2009
• http://dl.acm.org/citation.cfm?id=1553374.1553378&preflayout=flat#citedby

Quotes

Abstract

Users of topic modeling methods often have knowledge about the composition of words that should have high or low probability in various topics. We incorporate such domain knowledge using a novel Dirichlet Forest prior in a Latent Dirichlet Allocation framework. The prior is a mixture of Dirichlet tree distributions with special structures. We present its construction, and inference via collapsed Gibbs sampling. Experiments on synthetic and real datasets demonstrate our model's ability to follow and generalize beyond user-specified domain knowledge.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 IncorporatingDomainKnowledgeInt	Xiaojin Zhu Mark Craven David Andrzejewski			Incorporating Domain Knowledge Into Topic Modeling via Dirichlet Forest Priors				10.1145/1553374.1553378		2009