Interactive Concept Learning for Uncovering Latent Themes in Large Text
  Collections
Maria Leonor Pacheco Tunazzina Islam Lyle Ungar Ming Yin Dan Goldwasser
Experts across diverse disciplines are often interested in making sense of
large text collections. Traditionally, this challenge is approached either by
noisy unsupervised techniques such as topic models, or by following a manual
theme discovery process. In this paper, we expand the definition of a theme to
account for more than just a word distribution, and include generalized
concepts deemed relevant by domain experts. Then, we propose an interactive
framework that receives and encodes expert feedback at different levels of
abstraction. Our framework strikes a balance between automation and manual
coding, allowing experts to maintain control of their study while reducing the
manual effort required.