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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P17-1083.pdf