Papers by Justin Wood
A Bayesian Topic Model for Human-Evaluated Interpretability (2022.lrec-1)
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| Challenge: | Topic modeling is an effective way to analyze unstructured textual data. |
| Approach: | They propose to combine nonparametric and weakly-supervised topic models to produce interpretable topics. |
| Outcome: | The proposed model outperforms weakly-supervised models in the field of topic modeling. |