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k-cap_embedding_analysis

Code used for creating the visuals of the 'Do you catch my Drift?' K-CAP 2023 short-paper submission

https://github.com/ritten11/k-cap_embedding_analysis

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
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    Academic publication links
    Links to: zenodo.org
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    Academic email domains
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    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.3%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Code used for creating the visuals of the 'Do you catch my Drift?' K-CAP 2023 short-paper submission

Basic Info
  • Host: GitHub
  • Owner: Ritten11
  • Language: Jupyter Notebook
  • Default Branch: master
  • Size: 26.1 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 3
Created over 2 years ago · Last pushed over 2 years ago
Metadata Files
Readme Citation

README.md

DOI

Introduction

This notebook was used to create Figure 1 of the 'Do You Catch My Drift' short paper submission to the 2023 K-CAP conference (doi: https://doi.org/10.1145/3587259.3627555). While properly commented, the project remains a work in progress, meaning that some redundancies and inefficiencies are still present within the notebook. However, all code necessary to recreate a figure showing the implicit structure of the used embeddings is present. Note that the used t-SNE implementation is stochastic, meaning that each time the notebook is run, the resulting visualisation will slightly differ. This could be solved by setting the seed, but this has not been implemented yet.

Running instructions

  • Open a terminal and navigate to the directory in which this notebook is stored.
  • Ensure you have a working Latex installing (run tex --version). If not installed, the labels and titles within the figure will not be rendered properly.
  • (optional) Initialise a new python environment and activate this environment.
  • Run pip install -r requirements.txt
  • Run jupyter-notebook

Note 1: This notebook has been tested on python version 3.10.4

Note 2: The pickled files were pickled using protocol version 4.

Owner

  • Login: Ritten11
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Roothaert"
  given-names: "Ritten"
  orcid: "https://orcid.org/0009-0008-7843-6513"
title: "Camera-ready K-CAP submission of 'Do you catch my drift?' short paper"
version: camera-ready
doi: 10.5281/zenodo.10026567
date-released: 2023-10-20
url: "https://github.com/Ritten11/K-CAP_embedding_analysis"

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Dependencies

requirements.txt pypi
  • jupyterlab ==3.6.3
  • matplotlib ==3.7.1
  • numpy ==1.22.0
  • pandas ==1.5.3
  • rdflib ==6.2.0
  • scikit-learn ==1.2.1
  • seaborn ==0.12.2