Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/iswc2008/paper/research/334> ?p ?o. }
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- 334 creator america-holloway.
- 334 creator chaitanya-chemudugunta.
- 334 creator mark-steyvers.
- 334 creator padhraic-smyth.
- 334 type InProceedings.
- 334 label "Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning".
- 334 sameAs 334.
- 334 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 uses 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.".
- 334 hasAuthorList authorList.
- 334 hasTopic Semantic_Web.
- 334 isPartOf proceedings.
- 334 keyword "ontologies".
- 334 keyword "tagging".
- 334 keyword "topic models".
- 334 keyword "unsupervised learning".
- 334 title "Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning".