@InProceedings{lund-EtAl:2017:Long,
  author    = {Lund, Jeffrey  and  Cook, Connor  and  Seppi, Kevin  and  Boyd-Graber, Jordan},
  title     = {Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {896--905},
  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, go-
	ing beyond existing single word anchor
	algorithms—an approach we call “Tan-
	dem 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 interac-
	tive and non-interactive approaches. Tan-
	dem anchors are faster and more intuitive
	than existing interactive approaches.},
  url       = {http://aclweb.org/anthology/P17-1083}
}