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Experts across diverse disciplines are often interested in making sense oflarge text collections. Traditionally, this challenge is approached either bynoisy unsupervised techniques such as topic models, or by following a manualtheme discovery process. In this paper, we expand the definition of a theme toaccount for more than just a word distribution, and include generalizedconcepts deemed relevant by domain experts. Then, we propose an interactiveframework that receives and encodes expert feedback at different levels ofabstraction. Our framework strikes a balance between automation and manualcoding, allowing experts to maintain control of their study while reducing themanual effort required.

Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections