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[EMNLP2023] MixTEA: Semi-supervised Entity Alignment with Mixture Teaching

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MixTEA

Getting Started

Datasets

We use entity alignment benchmark datasets OpenEA which can be downloaded from OpenEA. You need to put the prepared data into /data folder.

Dependencies

  • Python 3
  • PyTorch
  • networkx==2.5.1
  • Scipy
  • Numpy
  • Pandas
  • Scikit-learn
  • Faiss

You can automatically download corresponding dependencies by following scripts:

pip install -r .\requirements.txt

Running

Note: The settings of hyper-parameters are given in /args folder.

To run MixTEA, please use the following scripts (ps: --task is an argument):

python run.py --task en_fr_15k
python run.py --task en_de_15k
python run.py --task d_w_15k
python run.py --task d_y_15k

To run 5-fold cross-validation, please use the following script:

python run_fold.py --task en_fr_15k

We also provide jupyter notebook version in MixTEA.ipynb.

If you have any difficulty or question in running code and reproducing experimental results, please email to xiefeng@nudt.edu.cn.

Acknowledgement

We refer to the codes of these repos: GCN-Align, OpenEA, MuGNN, MeanTeacher. Thanks for their great contributions!

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[EMNLP2023] MixTEA: Semi-supervised Entity Alignment with Mixture Teaching

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