Paper: Interactive Topic Modeling

ACL ID P11-1026
Title Interactive Topic Modeling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011

Topic models have been used extensively as a tool for corpus exploration, and a cottage in- dustry has developed to tweak topic models to better encode human intuitions or to better model data. However, creating such extensions requires expertise in machine learning unavail- able to potential end-users of topic modeling software. In this work, we develop a frame- work for allowing users to iteratively refine the topics discovered by models such as la- tent Dirichlet allocation (LDA) by adding con- straints that enforce that sets of words must ap- pear together in the same topic. We incorporate these constraints interactively by selectively removing elements in the state of a Markov Chain used for inference; we investigate a va- riety of methods for incorporating this infor- mation and demo...