https://github.com/EliasKB/Multilingual-Gaussian-Latent-Dirichlet-Allocation-MGLDA

https://github.com/EliasKB/Multilingual-Gaussian-Latent-Dirichlet-Allocation-MGLDA

Last synced: about 2 months ago
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• Host: GitHub
• URL: https://github.com/EliasKB/Multilingual-Gaussian-Latent-Dirichlet-Allocation-MGLDA
• Owner: EliasKB
• Created: 2019-12-25T23:09:32.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2019-12-28T15:29:20.000Z (about 5 years ago)
• Last Synced: 2024-08-04T01:30:51.017Z (5 months ago)
• Language: Python
• Size: 11.5 MB
• Stars: 4
• Watchers: 3
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-topic-models - MG-LDA - Python implementation of (Multi-lingual) Gaussian LDA [:page_facing_up:](https://raw.githubusercontent.com/EliasKB/Multilingual-Gaussian-Latent-Dirichlet-Allocation-MGLDA/master/MGLDA.pdf) (Models / Embedding based Topic Models)

README

        
# Multilingual Gaussian Latent Dirichlet Allocation MGLDA

# In the field of: 

## 1. Natural language processing (Text clustering and Topic modeling)

## 2. Bayasian statistics (mathematic science) and Machine learning (unsupervised learning)

### Master thesis, Chalmers universlity of technology, University of Gothenburg

### Author: Elias Kamyab, M.S. in Mathematical statisitcs, M.S. in Applied data science https://www.linkedin.com/in/elias-kamyab-71669014b/

#### Company: The thesis has been done by collaboration with Swedish Audio book company: Storytel, located in Stockholm, https://www.storytel.com/se/sv/

#### supervisor: Johan Jonasson, professor in Mathematical Sciences, Chalmers university of technology, Gothenburg

##### Copyright

###### The online availability of the document implies permanent permission for anyone to read, to download, or to print out single copies for his/hers own use and to use it unchanged for non-commercial research and educational purpose.

###### According to intellectual property law the author has the right to be mentioned when his/h

er work is accessed as described above and to be protected against infringement. 

###### For original paper about Gaussian Latent Dirichlet allocation https://github.com/rajarshd/Gaussian_LDA