| Getting Humans in the Loop | In general, the more words in the constraint, the more likely it was to noticeably affect the topic distribution . |
| Simulation Experiment | Specifically, for a corpus with M classes, we use the per-document topic distribution as a feature vector in a supervised classi- |
| Simulation Experiment | Next, we perform one of the strategies for state ablation, add additional iterations of Gibbs sampling, use the newly obtained topic distribution of each document as the feature vector, and perform classification on the test / train split. |
| Simulation Experiment | Doc ablation gives more freedom for the constraints to overcome the inertia of the old topic distribution and move towards a new one influenced by the constraints. |