https://github.com/bean5/paper-gc-tm-venue-entropy

Whitepaper on LDA Topic Models to compute topic entropy by year. Base corpus: LDS General Conference articles spanning decades.
https://github.com/bean5/paper-gc-tm-venue-entropy

ai general-conference latex lda lds nlp paper research topic-modeling topic-models whitepaper

Last synced: 8 months ago
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Whitepaper on LDA Topic Models to compute topic entropy by year. Base corpus: LDS General Conference articles spanning decades.

• Host: GitHub
• URL: https://github.com/bean5/paper-gc-tm-venue-entropy
• Owner: bean5
• Created: 2020-01-27T20:32:40.000Z (over 5 years ago)
• Default Branch: GC-TM-venue-entropy-5-page-final
• Last Pushed: 2024-05-23T18:27:36.000Z (over 1 year ago)
• Last Synced: 2025-01-02T07:26:35.420Z (10 months ago)
• Topics: ai, general-conference, latex, lda, lds, nlp, paper, research, topic-modeling, topic-models, whitepaper
• Language: TeX
• Homepage:
• Size: 358 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Theological topics through time: An application of Gibbs-sampled LDA and post-hoc metrics to compare religious venues

Whitepaper on LDA Topic Models to compute topic entropy by year. Base corpus: LDS General Conference articles spanning decades.

## Dependencies

- docker

- docker-compose

## Build Instructions

```sh

docker-compose run proposal

docker-compose run final

```

The first time you run the command it will need to build the latex base image which can take a long time.

## To Do

- [ ] Use a common base image to accelerate first-time PDF generation