Learning author-topic models from text corpora

doi:10.1145/1658377.1658381

Learning author-topic models from text corpora

Full Text:

PDF

Buy this Article

Authors:	Michal Rosen-Zvi	IBM Research Laboratory in Haifa
	Chaitanya Chemudugunta	University of California, Irvine
	Thomas Griffiths	University of California, Berkeley
	Padhraic Smyth	University of California, Irvine
	Mark Steyvers	University of California, Irvine

	2010 Article Research Refereed
Bibliometrics
· Downloads (6 Weeks): 33 · Downloads (12 Months): 221 · Downloads (cumulative): 1,104 · Citation Count: 15

Published in:

	· Journal
	ACM Transactions on Information Systems (TOIS) TOIS Homepage archive
	Volume 28 Issue 1, January 2010 Article No. 4 ACM New York, NY, USA table of contents doi>10.1145/1658377.1658381

Tools and Resources

Buy this Article
Request Permissions
TOC Service:
- Email
- RSS
Save to Binder
Export Formats:
- BibTeX
- EndNote
- ACM Ref

Share:

|

Tags: author models clustering gibbs sampling markup languages perplexity text analysis topic models unsupervised learning

Feedback | Switch to single page view (no tabs)

**Javascript is not enabled and is required for the "tabbed view" or switch to the single page view**

Powered by

The ACM Digital Library is published by the Association for Computing Machinery. Copyright © 2013 ACM, Inc.
Terms of Usage Privacy Policy Code of Ethics Contact Us

Useful downloads:

Adobe Acrobat

QuickTime

Windows Media Player

Real Player

The ACM Guide to Computing Literature

All Tags

Export Formats

Save to Binder