Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling
Jeffrey Lund, C. Paul Cook, Kevin D. Seppi, Jordan Lee Boyd-Graber · 2017
Interactive topic models are powerful tools for understanding 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 userfacing applications.We propose combinations of words as anchors, going beyond existing single word anchor algorithmsan 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 noninteractive approaches.Tandem anchors are faster and more intuitive than existing interactive approaches.