Latent Dirichlet Allocation (LDA) is a topic modeling tool that automatically discovers topics from a large collection of documents. It is one of the most popular text analysis tools currently in use. In practice however, the topics discovered by LDA do not always make sense to end users. In this extended abstract, we propose an active learning framework that interactively and iteratively acquires user feedback to improve the quality of learned topics. We conduct experiments to demonstrate its effectiveness with simulated user input on a benchmark dataset.
|Title of host publication||Active Learning with Constrained Topic Model|
|State||Published - 2014|
|Event||Workshop on Interactive Language Learning, Visualization, and Interfaces, ACL - Baltimore, MD|
Duration: Jun 1 2014 → …
|Conference||Workshop on Interactive Language Learning, Visualization, and Interfaces, ACL|
|Period||6/1/14 → …|