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Mining Entity Synonyms with Efficient Neural Set Generation

Documentation Status

This repo includes datasets, model training scripts, and model evaluation scripts used in paper -- Mining Entity Synonyms with Efficient Neural Set Generation.

Details about SynSetMine model can be accessed here, and this implementation is based on the PyTorch library.

The documents would be available here.

Installation

Simply clone this repository via

git clone https://github.com/mickeystroller/SynSetMine-pytorch.git
cd SynSetMine-pytorch

Check whether the below dependencies are satisfied. If not, simply install them via

pip install -r requirements_full.txt

Training Model

You can train SynSetMine model and test its performance using commands in run.sh

chmod +x run.sh
./run.sh

By default, we will run on NYT dataset. You can uncomment the code in run.sh to run on the other two datasets.

Model snapshots will be saved in ./snapshots/ directory. Logs will be saved in ./runs/ directory, and final results will be stored in ./results/ directory.

Loading Pre-trained Model for Prediction

We save three pre-trained models, one for each dataset in ./snapshots/ directory. You can load them directly for prediction via:

chmod +x predict.sh
./predict.sh

Dependencies

  • Python 3 with NumPy
  • PyTorch > 0.4.0
  • sklearn
  • tensorboardX (to display/log information while model running)
  • gensim (to load embedding files)
  • tqdm (to display information while model running)
  • networkx (to calculate one particular evaluation metric)

Screenshot

References

If you find this code useful for your research, please cite the following paper in your publication:

@inproceedings{Shen2019SynSetMine,
  title={Mining Entity Synonyms with Efficient Neural Set Generation},
  author={Jiaming Shen and Ruiilang Lv and Xiang Ren and Michelle Vanni and Brian Sadler and Jiawei Han},
  booktitle={AAAI},
  year={2019}
}

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PyTorch implementation of paper "Mining Entity Synonyms with Efficient Neural Set Generation" in AAAI 2019

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