2008 ModelingDocumentsbyCombiningSem

• (Chemudugunta et al., 2008) ⇒ Chaitanya Chemudugunta, America Holloway, Padhraic Smyth, and Mark Steyvers. (2008). “Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning.” In: Proceedings of the 7th International Conference on The Semantic Web. doi:10.1007/978-3-540-88564-1_15

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22Modeling+Documents+by+Combining+Semantic+Concepts+with+Unsupervised+Statistical+Learning%22+2008
• http://dl.acm.org/citation.cfm?id=1483155.1483175&preflayout=flat#citedby

Quotes

Author Keywords

• ontologies; tagging; topic models; unsupervised learning

Abstract

Human-defined concepts are fundamental building-blocks in constructing knowledge bases such as ontologies. Statistical learning techniques provide an alternative automated approach to concept definition, driven by data rather than prior knowledge. In this paper we propose a probabilistic modeling framework that combines both human-defined concepts and data-driven topics in a principled manner. The methodology we propose is based on applications of statistical topic models (also known as latent Dirichlet allocation models). We demonstrate the utility of this general framework in two ways. We first illustrate how the methodology can be used to automatically tag Web pages with concepts from a known set of concepts without any need for labeled documents. We then perform a series of experiments that quantify how combining human-defined semantic knowledge with data-driven techniques leads to better language models than can be obtained with either alone.

References

,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 ModelingDocumentsbyCombiningSem	Mark Steyvers Padhraic Smyth Chaitanya Chemudugunta America Holloway			Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning				10.1007/978-3-540-88564-1_15		2008