https://github.com/adjidieng/DETM

https://github.com/adjidieng/DETM

Last synced: 15 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/adjidieng/DETM
• Owner: adjidieng
• License: mit
• Created: 2019-09-23T14:36:06.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2021-02-02T23:47:33.000Z (over 3 years ago)
• Last Synced: 2024-02-23T23:37:24.958Z (4 months ago)
• Language: Python
• Size: 234 KB
• Stars: 128
• Watchers: 14
• Forks: 36
• Open Issues: 13
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-topic-models - D-ETM - Dynamic Embedded Topic Model [:page_facing_up:](https://arxiv.org/pdf/1907.05545.pdf) (Models / Embedding based Topic Models)
• awesome-stars - adjidieng/DETM - (Python)

README

        
# DETM

This is code that accompanies the paper titled "The Dynamic Embedded Topic Model" by Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei. (Arxiv link: https://arxiv.org/abs/1907.05545).

The DETM is an extension of the Embedded Topic Model (https://arxiv.org/abs/1907.04907) to corpora with temporal dependencies. The DETM models each word with a categorical distribution whose parameter is given by the inner product between the word embedding and an embedding representation of its assigned topic at a particular time step. The word embeddings allow the DETM to generalize to rare words. The DETM learns smooth topic trajectories by defining a random walk prior over the embeddings of the topics. The DETM is fit using structured amortized variational inference with LSTMs.

## Dependencies

+ python 3.6.7

+ pytorch 1.1.0

## Datasets

The pre-processed UN and ACL datasets can be found below:

+ https://bitbucket.org/franrruiz/data_acl_largev/src/master/

+ https://bitbucket.org/franrruiz/data_undebates_largev/src/master/

The pre-fitted embeddings can be found below:

+ https://bitbucket.org/diengadji/embeddings/src

All the scripts to pre-process a dataset can be found in the folder 'scripts'. 

## Example

To run the DETM on the ACL dataset you can run the command below. You can specify different values for other arguments, peek at the arguments list in main.py.

```

python main.py --dataset acl --data_path PATH_TO_DATA --emb_path PATH_TO_EMBEDDINGS --min_df 10 --num_topics 50 --lr 0.0001 --epochs 1000 --mode train

```

## Citation

```

@article{dieng2019dynamic,

  title={The Dynamic Embedded Topic Model},

  author={Dieng, Adji B and Ruiz, Francisco JR and Blei, David M},

  journal={arXiv preprint arXiv:1907.05545},

  year={2019}

}

```