Sometimes Average is Best: The Importance of Averaging for Prediction using MCMC Inference in Topic Modeling

Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik


Anthology ID:
D14-1182
Volume:
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
October
Year:
2014
Address:
Doha, Qatar
Editors:
Alessandro Moschitti, Bo Pang, Walter Daelemans
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1752–1757
Language:
URL:
https://aclanthology.org/D14-1182
DOI:
10.3115/v1/D14-1182
Bibkey:
Cite (ACL):
Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. 2014. Sometimes Average is Best: The Importance of Averaging for Prediction using MCMC Inference in Topic Modeling. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1752–1757, Doha, Qatar. Association for Computational Linguistics.
Cite (Informal):
Sometimes Average is Best: The Importance of Averaging for Prediction using MCMC Inference in Topic Modeling (Nguyen et al., EMNLP 2014)
Copy Citation:
PDF:
https://aclanthology.org/D14-1182.pdf