Awesome

MTAnchor

Repository contains demo code for MTAnchor, an interactive, multilingual topic modeling system. The code accompanies the paper Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages (Yuan et al., 2018).

Dependencies

Python 3
Numpy
Scipy
Scikit-learn
Flask
Flask_sqlalchemy
Flask_migrate
Anchor-topic

All above packages can be installed with pip install.

Setup

Clone repository: git clone https://github.com/forest-snow/mtanchor_demo.git
Move to folder: cd mtanchor_demo
Install dependencies: pip install -r requirements.txt
Run bash script: ./mtanchor.sh
Go to http://127.0.0.1:5000/ in your browser

Usage

Look at the most likely words for each topic in the blue boxes and the anchor words in the purple boxes.
Move words from blue boxes to purple boxes to add them as anchors (note that interface will not let you mix words from languages). You may also remove words from the purple boxes to delete them as anchors.
Hover over words to see their translation (if available).
Click on words to see occurrences of words and their translations highlighted across the interface.
Search for words and see what words exist in the corpora. Once you have selected a word from the dropdown menu, then you have the option of adding the word and/or its translation as an anchor to the topic.
Add or remove topics from the interface.
Update topics with your newly chosen anchors (note that interface will not let you update topics if no anchor word is chosen).
Restart your progress if you want to start over.
End app with Ctrl+C.

Memory space

The demo uses a SQLite database (stored in your local files) to save data. Since this is only a demo, only top 1000 words from each corpus is included in the vocabulary. The demo doesn't let user submit their results to prevent database from getting too large. If you find that the database is taking up too much space, run delete_db.sh to delete all data from the database.

Data

If you want the Wikipedia data used in the experiments, you may download it here. The dataset contains Chinese and English Wikipedia articles that are labeled in one of six categories: film, music, animals, politics, religion, and food. Please cite this paper if you use the data.

Citation

@inproceedings{yuan2018mtanchor,
  title={Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages},
  author={Yuan, Michelle and Van Durme, Benjamin and Boyd-Graber, Jordan},
  booktitle={Advances in neural information processing systems},
  year={2018}
}

License

Licensed under the terms of the MIT License. A full copy of the license can be found in LICENSE.