Statistical entity-topic models

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Statistical entity-topic models

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Philadelphia, PA, USA

POSTER SESSION: Research track posters table of contents

Pages: 680 - 686

Year of Publication: 2006

ISBN:1-59593-339-5

Authors

David Newman	Univ. of California, Irvine, CA
Chaitanya Chemudugunta	Univ. of California, Irvine, CA
Padhraic Smyth	Univ. of California, Irvine, CA

Sponsors

ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 16, Downloads (12 Months): 109, Citation Count: 7

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ABSTRACT

The primary purpose of news articles is to convey information about who, what, when and where. But learning and summarizing these relationships for collections of thousands to millions of articles is difficult. While statistical topic models have been highly successful at topically summarizing huge collections of text documents, they do not explicitly address the textual interactions between who/where, i.e. named entities (persons, organizations, locations) and what, i.e. the topics. We present new graphical models that directly learn the relationship between topics discussed in news articles and entities mentioned in each article. We show how these entity-topic models, through a better understanding of the entity-topic relationships, are better at making predictions about entities.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY 7

	Qiaozhu Mei , Xuehua Shen , ChengXiang Zhai, Automatic labeling of multinomial topic models, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, August 12-15, 2007, San Jose, California, USA
	Chong Wang , Jinggang Wang , Xing Xie , Wei-Ying Ma, Mining geographic knowledge using location aware topic model, Proceedings of the 4th ACM workshop on Geographical information retrieval, November 09-09, 2007, Lisbon, Portugal
	Fabian Mörchen , Mathäus Dejori , Dmitriy Fradkin , Julien Etienne , Bernd Wachmann , Markus Bundschus, Anticipating annotations and emerging trends in biomedical literature, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2008, Las Vegas, Nevada, USA
	Tatsuya Asou , Koji Eguchi, Predicting protein-protein relationships from literature using collapsed variational latent dirichlet allocation, Proceeding of the 2nd international workshop on Data and text mining in bioinformatics, October 30-30, 2008, Napa Valley, California, USA
	Hitohiro Shiozaki , Koji Eguchi , Takenao Ohkawa, Entity Network Prediction Using Multitype Topic Models, IEICE - Transactions on Information and Systems, v.E91-D n.11, p.2589-2598, November 2008
	Jonathan Chang , Jordan Boyd-Graber , David M. Blei, Connections between the lines: augmenting social networks with text, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, June 28-July 01, 2009, Paris, France
	Amr Ahmed , Eric P. Xing , William W. Cohen , Robert F. Murphy, Structured correspondence topic models for mining captioned figures in biological literature, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, June 28-July 01, 2009, Paris, France