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
Bibkey:
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)
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PDF:
https://aclanthology.org/D18-1095.pdf