Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality

Jey Han Lau, David Newman, Timothy Baldwin


Anthology ID:
E14-1056
Volume:
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics
Month:
April
Year:
2014
Address:
Gothenburg, Sweden
Editors:
Shuly Wintner, Sharon Goldwater, Stefan Riezler
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
530–539
Language:
URL:
https://aclanthology.org/E14-1056
DOI:
10.3115/v1/E14-1056
Bibkey:
Cite (ACL):
Jey Han Lau, David Newman, and Timothy Baldwin. 2014. Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, pages 530–539, Gothenburg, Sweden. Association for Computational Linguistics.
Cite (Informal):
Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality (Lau et al., EACL 2014)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-5/E14-1056.pdf
Code
 jhlau/topic_interpretability