Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling

Jeffrey Lund, Connor Cook, Kevin Seppi, Jordan Boyd-Graber


Abstract
Interactive topic models are powerful tools for those seeking to understand large collections of text. However, existing sampling-based interactive topic modeling approaches scale poorly to large data sets. Anchor methods, which use a single word to uniquely identify a topic, offer the speed needed for interactive work but lack both a mechanism to inject prior knowledge and lack the intuitive semantics needed for user-facing applications. We propose combinations of words as anchors, going beyond existing single word anchor algorithms—an approach we call “Tandem Anchors”. We begin with a synthetic investigation of this approach then apply the approach to interactive topic modeling in a user study and compare it to interactive and non-interactive approaches. Tandem anchors are faster and more intuitive than existing interactive approaches.
Anthology ID:
P17-1083
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
896–905
Language:
URL:
https://aclanthology.org/P17-1083
DOI:
10.18653/v1/P17-1083
Bibkey:
Cite (ACL):
Jeffrey Lund, Connor Cook, Kevin Seppi, and Jordan Boyd-Graber. 2017. Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 896–905, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling (Lund et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1083.pdf
Video:
 https://aclanthology.org/P17-1083.mp4