Prediction Focused Topic Models via Feature Selection

Jason Ren, Russell Kunes, Finale Doshi-Velez
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:4420-4429, 2020.

Abstract

Supervised topic models are often sought to balance prediction quality and interpretability. However, when models are (inevitably) misspecified, standard approaches rarely deliver on both. We introduce a novel approach, the prediction-focused topic model, that uses the supervisory signal to retain only vocabulary terms that improve, or at least do not hinder, prediction performance. By removing terms with irrelevant signal, the topic model is able to learn task-relevant, coherent topics. We demonstrate on several data sets that compared to existing approaches, prediction-focused topic models learn much more coherent topics while maintaining competitive predictions.

Cite this Paper


BibTeX
@InProceedings{pmlr-v108-ren20a, title = {Prediction Focused Topic Models via Feature Selection}, author = {Ren, Jason and Kunes, Russell and Doshi-Velez, Finale}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {4420--4429}, year = {2020}, editor = {Silvia Chiappa and Roberto Calandra}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/ren20a/ren20a.pdf}, url = { http://proceedings.mlr.press/v108/ren20a.html }, abstract = {Supervised topic models are often sought to balance prediction quality and interpretability. However, when models are (inevitably) misspecified, standard approaches rarely deliver on both. We introduce a novel approach, the prediction-focused topic model, that uses the supervisory signal to retain only vocabulary terms that improve, or at least do not hinder, prediction performance. By removing terms with irrelevant signal, the topic model is able to learn task-relevant, coherent topics. We demonstrate on several data sets that compared to existing approaches, prediction-focused topic models learn much more coherent topics while maintaining competitive predictions.} }
Endnote
%0 Conference Paper %T Prediction Focused Topic Models via Feature Selection %A Jason Ren %A Russell Kunes %A Finale Doshi-Velez %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-ren20a %I PMLR %P 4420--4429 %U http://proceedings.mlr.press/v108/ren20a.html %V 108 %X Supervised topic models are often sought to balance prediction quality and interpretability. However, when models are (inevitably) misspecified, standard approaches rarely deliver on both. We introduce a novel approach, the prediction-focused topic model, that uses the supervisory signal to retain only vocabulary terms that improve, or at least do not hinder, prediction performance. By removing terms with irrelevant signal, the topic model is able to learn task-relevant, coherent topics. We demonstrate on several data sets that compared to existing approaches, prediction-focused topic models learn much more coherent topics while maintaining competitive predictions.
APA
Ren, J., Kunes, R. & Doshi-Velez, F.. (2020). Prediction Focused Topic Models via Feature Selection. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:4420-4429 Available from http://proceedings.mlr.press/v108/ren20a.html .

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