https://github.com/mirkobunse/regularized-oq

Supplementary material for our paper "Regularization-Based Methods for Ordinal Quantification"
https://github.com/mirkobunse/regularized-oq

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Supplementary material for our paper "Regularization-Based Methods for Ordinal Quantification"

• Host: GitHub
• URL: https://github.com/mirkobunse/regularized-oq
• Owner: mirkobunse
• Created: 2023-07-17T08:10:54.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-27T11:18:49.000Z (9 months ago)
• Last Synced: 2024-03-27T12:34:09.444Z (9 months ago)
• Language: Julia
• Size: 1.35 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Supplementary material for "Regularization-Based Methods for Ordinal Quantification"

The `supplement.pdf` contains additional material, e.g., extended experimental results.

The directories `jl/` and `py/` contain the source code of our methods and experiments, as written in Julia and Python, respectively. Please consult their `jl/README.md` and `py/README.md` files for more information.

- the Python code implements the extraction of the Amazon-OQ-BK dataset and the ordinal classifier experiment (Tab. 1 in our paper).

- the Julia code implements the extraction of the FACT-OQ dataset and the comparison experiment (Tab. 2 in our paper).

We use two programming languages because we could build, for the respective tasks, on existing, public code: [QuaPy](https://github.com/HLT-ISTI/QuaPy) and [CherenkovDeconvolution.jl](https://github.com/mirkobunse/CherenkovDeconvolution.jl). We further thank the authors of [mord](https://github.com/fabianp/mord) for making their code publicly available.