Labeled Anchors and a Scalable, Transparent, and Interactive Classifier
Jeffrey Lund, Stephen Cowley, Wilson Fearn, Emily Hales, Kevin Seppi
Abstract
We propose Labeled Anchors, an interactive and supervised topic model based on the anchor words algorithm (Arora et al., 2013). Labeled Anchors is similar to Supervised Anchors (Nguyen et al., 2014) in that it extends the vector-space representation of words to include document labels. However, our formulation also admits a classifier which requires no training beyond inferring topics, which means our approach is also fast enough to be interactive. We run a small user study that demonstrates that untrained users can interactively update topics in order to improve classification accuracy.- Anthology ID:
- D18-1095
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 824–829
- Language:
- URL:
- https://aclanthology.org/D18-1095
- DOI:
- 10.18653/v1/D18-1095
- Cite (ACL):
- Jeffrey Lund, Stephen Cowley, Wilson Fearn, Emily Hales, and Kevin Seppi. 2018. Labeled Anchors and a Scalable, Transparent, and Interactive Classifier. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 824–829, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Labeled Anchors and a Scalable, Transparent, and Interactive Classifier (Lund et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/D18-1095.pdf