Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections

Jingwei Zhang, Aaron Gerow, Jaan Altosaar, James Evans, Richard Jean So


Anthology ID:
D15-1179
Volume:
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2015
Address:
Lisbon, Portugal
Editors:
Lluís Màrquez, Chris Callison-Burch, Jian Su
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1554–1564
Language:
URL:
https://aclanthology.org/D15-1179
DOI:
10.18653/v1/D15-1179
Bibkey:
Cite (ACL):
Jingwei Zhang, Aaron Gerow, Jaan Altosaar, James Evans, and Richard Jean So. 2015. Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1554–1564, Lisbon, Portugal. Association for Computational Linguistics.
Cite (Informal):
Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections (Zhang et al., EMNLP 2015)
Copy Citation:
PDF:
https://aclanthology.org/D15-1179.pdf
Code
 iceboal/correlated-lda