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Software . 2020
License: https://opensource.org/licenses/MIT
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Software . 2020
License: https://opensource.org/licenses/MIT
Data sources: Datacite
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License: http://opensource.org/licenses/MIT
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Prediction Constrained Topic Models

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 15 Apr 2020 English Publisher:Zenodo
Authors: Hughes, Michael C.;

doi: 10.5281/zenodo.3753535 , 10.5281/zenodo.3753536

Prediction Constrained Topic Models

Abstract

Open source software for training and evaluating prediction-constrained topic models, and comparing these to baseline classification methods (such as logistic regression or ensembles of decision trees). Available on Github: https://github.com/dtak/prediction-constrained-topic-models/ Underlying methods have been published in the AISTATS 2018 paper: "Prediction-constrained semi-supervised topic models" M. C. Hughes, L. Weiner, G. Hope, T. H. McCoy, R. H. Perlis, E. B. Sudderth, and F. Doshi-Velez. Artificial Intelligence & Statistics (AISTATS), 2018. Paper PDF: https://www.michaelchughes.com/papers/HughesEtAl_AISTATS_2018.pdf Supplement PDF: https://www.michaelchughes.com/papers/HughesEtAl_AISTATS_2018_supplement.pdf

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Keywords

supervised topic model, prediction-constrained training

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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