@inproceedings{lund-etal-2017-tandem,
    title = "Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling",
    author = "Lund, Jeffrey  and
      Cook, Connor  and
      Seppi, Kevin  and
      Boyd-Graber, Jordan",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P17-1083/",
    doi = "10.18653/v1/P17-1083",
    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, 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."
}